Add articles/ipol_style_guide/

Add articles/Hierarchical Data Format - Wikipedia/
Add debugging
2024-07-24 00:23:39 +02:00 · 2024-07-24 00:23:01 +02:00 · 2024-07-24 00:22:08 +02:00 · 2024-07-24 00:21:09 +02:00 · 2024-07-24 00:20:45 +02:00 · 2024-07-04 04:18:13 +02:00
46 changed files with 2841 additions and 89 deletions
--- a/algorithms/image_utils/image_utils.py
+++ b/algorithms/image_utils/image_utils.py
@@ -30,12 +30,12 @@ def getPrnuShownAsSuch(size, gaussianNoise = 0):
            break
    # Center vertically, especially in the case `HEIGHT` > `WIDTH`.
    draw.text((0, HEIGHT // 2 - fontSize // 2), TEXT, 255, font = font)
-    imageNpArray = np.array(imagePil)
+    imageNpArray = np.array(imagePil, dtype = np.float64)
    gaussianNoiseNpArray = randomGaussianImage(gaussianNoise, size[::-1])
    #prnuShownAsSuch = imageNpArray + gaussianNoiseNpArray
    prnuShownAsSuch = imageNpArray
    for y in range(HEIGHT):
        for x in range(WIDTH):
            if prnuShownAsSuch[y, x] != 0:
-                prnuShownAsSuch[y, x] += gaussianNoiseNpArray[y, x]
-    return prnuShownAsSuch
+                prnuShownAsSuch[y, x] = gaussianNoiseNpArray[y, x]
+    return prnuShownAsSuch
--- a/Wikipedia.pdf.txt
+++ b/Wikipedia.pdf.txt
@@ -0,0 +1 @@
+https://en.wikipedia.org/w/index.php?title=Color_balance&oldid=1193262662
--- a/Wikipedia.xopp.xml
+++ b/Wikipedia.xopp.xml
@@ -0,0 +1,60 @@
+<?xml version="1.0" standalone="no"?>
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+  <title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
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--- a/Wikipedia.pdf.txt
+++ b/Wikipedia.pdf.txt
@@ -0,0 +1 @@
+https://en.wikipedia.org/w/index.php?title=Color_filter_array&oldid=1225070782
--- a/Wikipedia.xopp.xml
+++ b/Wikipedia.xopp.xml
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+  <title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
+  <page width="596.00000000" height="842.00000000">
+    <background type="pdf" domain="absolute" filename="/home/benjamin/Desktop/bens_folder/school/ens/asp/aria/internship/work/articles/Color filter array - Wikipedia/Color filter array - Wikipedia.pdf" pageno="1"/>
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+      <text font="Sans" size="12.00000000" x="46.91696167" y="217.29287720" color="#00c0ffff" ts="0" fn="">why using such method for e paper? is not it the case
+for usual displays?</text>
+    </layer>
+  </page>
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+    <background type="pdf" pageno="2"/>
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+  </page>
+  <page width="596.00000000" height="842.00000000">
+    <background type="pdf" pageno="7"/>
+    <layer/>
+  </page>
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--- a/Wikipedia/Cramér–Rao
+++ b/Wikipedia/Cramér–Rao
@@ -0,0 +1 @@
+https://en.wikipedia.org/w/index.php?title=Cram%C3%A9r%E2%80%93Rao_bound&oldid=1223930930
--- a/Wikipedia/Cramér–Rao
+++ b/Wikipedia/Cramér–Rao
@@ -0,0 +1,58 @@
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+<xournal creator="Xournal++ 1.1.3" fileversion="4">
+  <title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
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--- a/Wikipedia.pdf.txt
+++ b/Wikipedia.pdf.txt
@@ -0,0 +1 @@
+https://en.wikipedia.org/w/index.php?title=Depth_of_field&oldid=1207712571
--- a/Wikipedia.xopp.xml
+++ b/Wikipedia.xopp.xml
@@ -0,0 +1,58 @@
+<?xml version="1.0" standalone="no"?>
+<xournal creator="Xournal++ 1.1.3" fileversion="4">
+  <title>Xournal++ document - see https://github.com/xournalpp/xournalpp</title>
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--- a/articles/Determining
+++ b/articles/Determining
@@ -1 +1 @@
-https://ws2.binghamton.edu/fridrich/Research/DoubleColumnFinal.pdf
+https://web.archive.org/web/20221208035521id_/https://ws2.binghamton.edu/fridrich/Research/DoubleColumnFinal.pdf
--- a/articles/Determining
+++ b/articles/Determining
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      <text font="Sans" size="12.00000000" x="15.95944214" y="19.01628113" color="#00c0ffff" ts="0" fn="">no occurrences of git, github and gitlab, code, script, Python
 not interesting occurrences of source</text>
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+
+PRNU estimation using (6) (have to investigate the denoiser used)
+pre-process PRNU estimation:
+- remove for each row and column its mean
+- if visually identifiable patterns in ZM(K), then Wiener filter in Fourier domain
+- high-pass filtering in the Fourier domain</text>
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+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">47.97225952 509.77767944 172.58543579 509.77767944</stroke>
+      <text font="Sans" size="12.00000000" x="263.93231201" y="435.04779053" color="#00c0ffff" ts="0" fn="">to check</text>
+      <text font="Sans" size="12.00000000" x="167.76638794" y="520.64541626" color="#00c0ffff" ts="0" fn="">why?</text>
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+      <text font="Sans" size="12.00000000" x="167.63052368" y="618.00769043" color="#00c0ffff" ts="0" fn="">why?</text>
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+      <stroke tool="pen" ts="0" fn="" color="#00c0ffff" width="1.41000000">194.06729126 560.01876831 201.72512817 548.12371826</stroke>
+      <teximage text="\text{why } E\left[\sum_{k=1}^N(\mathbf{I}_k)^2\right] = \sum_{k=1}^N(\mathbf{I}_k)^2?" left="183.49615479" top="591.16033936" right="300.89061988" bottom="625.16033936"/>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">57.79257202 685.46777344 194.09613044 685.46777344</stroke>
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+      <text font="Sans" size="12.00000000" x="2.33728027" y="715.83953857" color="#00c0ffff" ts="0" fn="">so there is no better *distance* to attribute a source camera or I am misunderstanding the purpose
+of the ML?</text>
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+      <text font="Sans" size="12.00000000" x="317.21777344" y="8.99786377" color="#00c0ffff" ts="0" fn="">in theory we would not like any saturation,
+is there a physical sensor limitation avoiding
+saturation?</text>
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+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">536.76416016 124.47381592 558.82409668 126.14575195</stroke>
+      <text font="Sans" size="12.00000000" x="325.64184570" y="125.86141968" color="#00c0ffff" ts="0" fn="">just describing these terms?</text>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">325.91107178 140.19284058 424.24843089 140.19284058</stroke>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">484.68475342 140.44500732 562.98525342 140.44500732</stroke>
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+      <text font="Sans" size="12.00000000" x="375.60253906" y="70.74871826" color="#00c0ffff" ts="0" fn="">to check from (7) but makes sense</text>
+      <text font="Sans" size="12.00000000" x="319.67297363" y="138.03695679" color="#00c0ffff" ts="0" fn="">seems to make sense but what precise
+argument is used here?</text>
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+      <text font="Sans" size="12.00000000" x="387.70733643" y="225.17886353" color="#00c0ffff" ts="0" fn="">argument?</text>
+      <text font="Sans" size="12.00000000" x="427.26794434" y="200.27055359" color="#00c0ffff" ts="0" fn="">argument?</text>
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+      <text font="Sans" size="12.00000000" x="291.08165394" y="565.81793919" color="#00c0ffff" ts="0" fn="">it seems that the gain is only introduced at the color
+interpolation step</text>
+      <text font="Sans" size="12.00000000" x="474.48402594" y="649.18218944" color="#00c0ffff" ts="0" fn="">like?</text>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">474.84166169 699.74548088 561.62416116 699.74548088</stroke>
+      <text font="Sans" size="12.00000000" x="318.42626146" y="675.00273397" color="#00c0ffff" ts="0" fn="">related to DSNU row/column artifacts?</text>
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+    </layer>
+  </page>
+  <page width="595.27559100" height="841.88976400">
+    <background type="solid" color="#ffffffff" style="plain"/>
+    <layer>
+      <teximage text="= \sum_{k=1}^N (\mathbf{I}_k)^2(\mathbf{W}_k / \mathbf{I}_k - \mathbf{K})" left="3.44375610" top="-3.67115784" right="119.75775610" bottom="37.48584216"/>
+      <teximage text="= \sum_{k=1}^N \mathbf{I}_k(\mathbf{W}_k - \mathbf{K}\mathbf{I}_k)" left="3.23849487" top="30.28213501" right="104.01349487" bottom="71.43913501"/>
+      <teximage text="\implies \sum_{k=1}^N \frac{\mathbf{W}_k / \mathbf{I}_k - \mathbf{K}}{1 / (\mathbf{I}_k)^2} = 0" left="153.24240112" top="-1.43804932" right="276.02523272" bottom="39.56195068"/>
+      <teximage text="\iff \sum_{k=1}^N \frac{\mathbf{W}_k / \mathbf{I}_k}{1 / (\mathbf{I}_k)^2} - \sum_{k=1}^N \frac{\mathbf{K}}{1 / (\mathbf{I}_k)^2} = 0" left="149.72015381" top="31.34738159" right="316.63015381" bottom="72.50438159"/>
+      <teximage text="\iff \sum_{k=1}^N \frac{\mathbf{W}_k / \mathbf{I}_k}{1 / (\mathbf{I}_k)^2} = \sum_{k=1}^N \frac{\mathbf{K}}{1 / (\mathbf{I}_k)^2}" left="145.67276001" top="66.19903564" right="295.42476001" bottom="107.35603564"/>
+      <teximage text="\iff \sum_{k=1}^N \frac{\mathbf{W}_k / \mathbf{I}_k}{1 / (\mathbf{I}_k)^2} = \mathbf{K} \sum_{k=1}^N \frac{1}{1 / (\mathbf{I}_k)^2}" left="142.56359863" top="106.72155762" right="302.95659863" bottom="147.87855762"/>
+      <teximage text="\iff \mathbf{K} = \frac{\sum_{k=1}^N \frac{\mathbf{W}_k / \mathbf{I}_k}{1 / (\mathbf{I}_k)^2}}{\sum_{k=1}^N \frac{1}{1 / (\mathbf{I}_k)^2}}" left="142.22589111" top="148.70774841" right="258.83289111" bottom="191.65474841"/>
+      <teximage text="\iff \mathbf{K} = \frac{\sum_{k=1}^N \mathbf{W}_k \mathbf{I}_k}{\sum_{k=1}^N (\mathbf{I}_k)^2}" left="142.66387939" top="187.59588623" right="256.17248725" bottom="225.59588623"/>
+      <teximage text="\text{by linearity of expected value, my actual question is why } \sum_{k=1}^NE[(\mathbf{I}_k)^2] = \sum_{k=1}^N(\mathbf{I}_k)^2?" left="311.52549129" top="-2.28384399" right="596.12817236" bottom="61.96316552"/>
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+      <text font="Sans" size="12.00000000" x="135.44167763" y="306.34509378" color="#00c0ffff" ts="0" fn="">same for rows</text>
+      <text font="Sans" size="12.00000000" x="104.01898849" y="279.02986889" color="#00c0ffff" ts="0" fn="">only b), no? and maybe not even completelly</text>
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+      <text font="Sans" size="12.00000000" x="46.73948682" y="390.13703149" color="#00c0ffff" ts="0" fn="">argument?</text>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">206.91503099 415.53977777 284.60588323 415.53977777</stroke>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">48.14256400 427.59472555 252.23296976 427.59472555</stroke>
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+      <text font="Sans" size="12.00000000" x="230.99488818" y="465.46172843" color="#00c0ffff" ts="0" fn="">like usual square, right? No along rows and columns? well it
+seems clear to me, if it was 4 there would be doubt but it is
+not the case</text>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">82.19327060 489.58326608 115.80655710 489.58326608</stroke>
+      <text font="Sans" size="12.00000000" x="295.42438097" y="140.58574078" color="#00c0ffff" ts="0" fn="">I should display such one to have an idea what it
+looks like</text>
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+      <text font="Sans" size="12.00000000" x="324.50536302" y="542.32024894" color="#00c0ffff" ts="0" fn="">could reproduce</text>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">396.68231050 627.94562726 561.77618422 627.94562726</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">325.05266174 640.95615355 365.77332735 640.95615355</stroke>
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+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">351.95635331 478.41970901 360.06030879 481.32268732</stroke>
+      <stroke tool="pen" ts="0" fn="" color="#00c0ffff" width="1.41000000">368.16078375 483.04682960 418.11455767 483.04682960</stroke>
+      <stroke tool="pen" ts="0" fn="" color="#00c0ffff" width="1.41000000">490.41743003 470.08336595 531.59094070 470.08336595</stroke>
+      <stroke tool="pen" ts="0" fn="" color="#00c0ffff" width="1.41000000">476.05005690 377.71796865 510.03202455 377.71796865</stroke>
+      <stroke tool="pen" ts="0" fn="" color="#00c0ffff" width="1.41000000">365.32673481 389.18784906 403.18625436 389.18784906</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">406.50624677 384.14855200 523.58813178 384.14855200</stroke>
+      <text font="Sans" size="12.00000000" x="321.91570692" y="447.04169660" color="#00c0ffff" ts="0" fn="">decrease in all color channels for all operations</text>
+      <text font="Sans" size="12.00000000" x="259.42894802" y="535.67410051" color="#00c0ffff" ts="0" fn="">decrease absolutely in all color channels for all operations</text>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">465.24486699 394.97940442 477.79275438 394.97940442</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">502.72938121 393.84499542 519.72079216 393.84499542</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">541.31948928 393.78844963 553.55036518 393.78844963</stroke>
+      <text font="Sans" size="12.00000000" x="385.93946725" y="438.38111058" color="#ff00ffff" ts="0" fn="">missing space?</text>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">367.55673747 407.32051124 372.86444471 408.74270711</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">349.29047892 421.19508854 375.93328303 422.38043162</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">331.84249500 436.94917033 371.81468761 436.94917033</stroke>
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+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">83.78749879 500.96337184 285.33152357 500.96337184</stroke>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">50.19340830 515.33986241 238.75907524 515.33986241</stroke>
+      <text font="Sans" size="12.00000000" x="1.99971551" y="501.09241328" color="#00c0ffff" ts="0" fn="">is a method proposed to automatize such identification?</text>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">49.71749802 533.92424780 283.86110544 533.92424780</stroke>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">48.92813943 547.51584392 258.93746195 547.51584392</stroke>
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+      <text font="Sans" size="12.00000000" x="162.27111816" y="582.22521065" color="#00c0ffff" ts="0" fn="">values?</text>
+      <stroke tool="pen" ts="0" fn="" color="#00c0ffff" width="1.41000000">282.54162497 566.05502042 285.74506271 568.38969081</stroke>
+      <stroke tool="pen" ts="0" fn="" color="#00c0ffff" width="1.41000000">285.74506271 568.38969081 289.28853185 561.35158066</stroke>
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+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">48.40104284 638.26755089 179.28073398 638.26755089</stroke>
+      <text font="Sans" size="12.00000000" x="39.45615154" y="618.35383644" color="#00c0ffff" ts="0" fn="">what about something like an histogram?</text>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">207.12328194 586.54394229 287.11063869 586.54394229</stroke>
+      <text font="Sans" size="12.00000000" x="80.33303505" y="597.63020665" color="#00c0ffff" ts="0" fn="">?</text>
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+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">50.58475211 614.63315349 139.33713166 614.63315349</stroke>
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+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">159.97735646 664.79083605 283.28571844 664.79083605</stroke>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">52.20492024 676.06579489 282.52852665 676.06579489</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">51.43394785 688.01198912 171.56945370 688.01198912</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">199.47988967 687.82035891 282.75887932 687.82035891</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">50.20028106 699.71590909 287.50250070 699.71590909</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">47.27143847 712.15310845 70.40158785 712.15310845</stroke>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">103.35564732 710.89154708 160.10100474 710.89154708</stroke>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">189.26515185 710.79883821 284.54188281 710.79883821</stroke>
+      <stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">50.59094390 723.90701672 253.79754975 723.90701672</stroke>
+      <text font="Sans" size="12.00000000" x="56.83878603" y="373.35349702" color="#00c0ffff" ts="0" fn="">linear pattern no more used in the paper remaining?</text>
+      <teximage text="corr?\\ \text{The only other occurrence is in VI. D.}" left="327.64978027" top="349.43379211" right="447.58863276" bottom="370.27279020"/>
+      <text font="Sans" size="12.00000000" x="462.96081543" y="350.33961487" color="#00c0ffff" ts="0" fn="">I would say so</text>
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+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">325.80708979 433.14406080 450.31307182 433.14406080</stroke>
+      <text font="Sans" size="12.00000000" x="493.49877514" y="457.36006076" color="#00c0ffff" ts="0" fn="">should investigate</text>
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+      <text font="Sans" size="12.00000000" x="389.90006581" y="432.13162644" color="#00c0ffff" ts="0" fn="">what about raw images?</text>
+      <text font="Sans" size="12.00000000" x="329.83006903" y="407.93830791" color="#00c0ffff" ts="0" fn="">not the same 3 as in 3x3 on page 5, right?
+or it means same variance 3 in 2 axes?</text>
+    </layer>
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+      <text font="Sans" size="12.00000000" x="49.85240131" y="299.48938750" color="#00c0ffff" ts="0" fn="">maybe not what I currently focus on</text>
+    </layer>
  </page>
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@@ -85,11 +791,36 @@ not interesting occurrences of source</text>
  </page>
  <page width="612.00000000" height="792.00000000">
    <background type="pdf" pageno="12"/>
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+      <text font="Sans" size="12.00000000" x="49.36018785" y="425.39423025" color="#00c0ffff" ts="0" fn="">not what I currently focus on</text>
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@@ -105,10 +836,24 @@ not interesting occurrences of source</text>
  </page>
  <page width="612.00000000" height="792.00000000">
    <background type="pdf" pageno="17"/>
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+    <layer>
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+      <text font="Sans" size="12.00000000" x="5.15356417" y="609.08074222" color="#00c0ffff" ts="0" fn="">all authors
+of this paper</text>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">196.39434405 610.98881787 285.41510258 610.98881787</stroke>
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  </page>
 </xournal>
--- a/articles/Digital
+++ b/articles/Digital
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+      <text font="Sans" size="12.00000000" x="452.09175872" y="378.60406499" color="#00c0ffff" ts="0" fn="">see (34)</text>
+      <text font="Sans" size="12.00000000" x="479.99473016" y="361.09989525" color="#00c0ffff" ts="0" fn="">see (26)</text>
+      <text font="Sans" size="12.00000000" x="308.01572735" y="389.19037438" color="#00c0ffff" ts="0" fn="">see (18)</text>
+      <stroke tool="pen" ts="0" fn="" color="#00c0ffff" width="1.41000000">347.42907632 371.46938447 343.90175346 384.63353262</stroke>
+      <text font="Sans" size="12.00000000" x="365.57879639" y="565.35534668" color="#00c0ffff" ts="0" fn="">according to Marina demo it is sPCE</text>
+      <text font="Sans" size="12.00000000" x="298.39611816" y="609.24667358" color="#00c0ffff" ts="0" fn="">what shape and size typically? 11 sized square
+chose Marina</text>
+      <text font="Sans" size="12.00000000" x="311.70404053" y="544.69454956" color="#00c0ffff" ts="0" fn="">why (0, 0) and not center? more generally
+how explain this formula?
+well with Marina np.roll there is such shift</text>
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@@ -1982,6 +2031,17 @@ have not achieved improvements as authors, right?</text>
      <text font="Sans" size="11.00000000" x="312.11756340" y="438.97829411" color="#00c0ffff" ts="0" fn="">do they provide the MATLAB code?
 No interesting occurrences of code, source (code), git,
 implementation, web</text>
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+      <text font="Sans" size="12.00000000" x="459.73004944" y="174.25554168" color="#00c0ffff" ts="0" fn="">how?</text>
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--- a/Wikipedia.pdf.txt
+++ b/Wikipedia.pdf.txt
@@ -0,0 +1 @@
+https://en.wikipedia.org/w/index.php?title=Prior_knowledge_for_pattern_recognition&oldid=1166323826
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+      <text font="Sans" size="12.00000000" x="195.21855164" y="400.61158752" color="#00c0ffff" ts="0" fn="">unclear meaning</text>
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+      <text font="Sans" size="12.00000000" x="315.56872476" y="138.42515356" color="#00c0ffff" ts="0" fn="">as a shift of 1 pixel makes the thing incorrect
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+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">312.15770120 293.06231834 462.90859273 293.06231834</stroke>
+      <text font="Sans" size="12.00000000" x="317.81186368" y="307.05381362" color="#00c0ffff" ts="0" fn="">means quadratic complexity?</text>
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+      <text font="Sans" size="12.00000000" x="454.34973017" y="388.78474864" color="#00c0ffff" ts="0" fn="">?</text>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">314.45628494 401.28864345 442.51318098 401.28864345</stroke>
+      <text font="Sans" size="12.00000000" x="528.65049681" y="411.54540916" color="#00c0ffff" ts="0" fn="">?</text>
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+      <text font="Sans" size="12.00000000" x="307.10767881" y="507.50371372" color="#00c0ffff" ts="0" fn="">?</text>
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--- a/documentation/skimage.restoration
+++ b/documentation/skimage.restoration
@@ -154,11 +154,30 @@
  </page>
  <page width="596.00000000" height="842.00000000">
    <background type="pdf" pageno="28"/>
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+    <layer>
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    <background type="pdf" pageno="29"/>
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+    <layer>
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+      <text font="Sans" size="12.00000000" x="171.11151123" y="766.00430298" color="#ff00ffff" ts="0" fn="">typo?</text>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">103.37088013 167.90295410 176.51040691 167.90295410</stroke>
+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">150.68096924 190.94442749 238.92777584 190.94442749</stroke>
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@@ -174,7 +193,33 @@
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+      <stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">290.49273682 342.56427002 482.17780067 342.56427002</stroke>
+      <teximage text="\text{similar to above \texttt{unsupervised\_wiener}}" left="189.02984619" top="311.98767090" right="370.04684619" bottom="331.08267090"/>
+      <text font="Sans" size="12.00000000" x="262.58197021" y="365.19287109" color="#00c0ffff" ts="0" fn="">new:</text>
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+      <text font="Sans" size="12.00000000" x="395.27593994" y="422.95880127" color="#00c0ffff" ts="0" fn="">?</text>
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--- a/datasets/noise_free_test_images/estimate_prnu.py
+++ b/datasets/noise_free_test_images/estimate_prnu.py
@@ -24,7 +24,7 @@ datasetPath = 'no_noise_images'
 # Note that contrarily to `datasets/fake/`, here we do not have images being Gaussian with `scale` `1` but actual images with pixel values between 0 and 255.
 # In addition to the range difference, note that the distribution in the first set of images was a Gaussian and here is very different and specific.
 PRNU_FACTOR = 0.01
-NOISE_FACTOR = 0.1
+NOISE_FACTOR = 0.2

 np.random.seed(0)

@@ -34,7 +34,12 @@ SPLIT_N_X_N_S = [1, 2, 4]
 fig, axes = plt.subplots(2, 4)
 fig.suptitle('PRNU estimation with different number of images having Gaussian noise and Gaussian noised PRNU')

+def axisImShow(axis, im):
+    imShow = axis.imshow(im)
+    plt.colorbar(imShow, label = 'Intensity', ax = axis, orientation = 'horizontal')
+
 for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
+    print(f'{splitNXN=}')
    IMAGE_SIZE_SHAPE = [dimension // splitNXN for dimension in (704, 469)]

    #prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
@@ -55,10 +60,13 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
            m = IMAGE_SIZE_SHAPE[1]
            n = IMAGE_SIZE_SHAPE[0]

-            imageWithoutPrnuNpArrayTiles = [imageWithoutPrnuNpArray[x : x + m, y : y + n] for x in range(0, imageWithoutPrnuNpArray.shape[0], m) for y in range(0, imageWithoutPrnuNpArray.shape[1], n)]
+            imageWithoutPrnuNpArrayTiles = [imageWithoutPrnuNpArray[x * m : (x + 1) * m, y * n : (y + 1) * n] for x in range(splitNXN) for y in range(splitNXN)]
+            #print(f'{m=} {n=} {0 * m=} {0 * (m + 1)=}')
+            #print([imageWithoutPrnuNpArrayTile.shape for imageWithoutPrnuNpArrayTile in imageWithoutPrnuNpArrayTiles])
            for imageWithoutPrnuNpArrayTile in imageWithoutPrnuNpArrayTiles:
                #print(imageWithoutPrnuNpArrayTile.shape, tuple(IMAGE_SIZE_SHAPE[::-1]))
                #if imageWithoutPrnuNpArrayTile.shape != tuple(IMAGE_SIZE_SHAPE[::-1]):
+                #    exit(1)
                #    continue
                imageNoise = randomGaussianImage(scale = 255 * NOISE_FACTOR, size = imageWithoutPrnuNpArrayTile.shape)
                imageWithPrnuNpArray = imageWithoutPrnuNpArrayTile + prnuNpArray + imageNoise
@@ -67,16 +75,19 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
                    axis = axes[0]

                    axis[0].set_title('First image without noise')
-                    axis[0].imshow(imageWithoutPrnuNpArrayTile)
+                    axisImShow(axis[0], imageWithoutPrnuNpArrayTile)

-                    axis[1].set_title('Actual Gaussian noised PRNU')
-                    axis[1].imshow(prnuNpArray)
+                    axis[1].set_title('First image Gaussian noise')
+                    axisImShow(axis[1], imageNoise)

-                    axis[2].set_title('F. i. with G. n.')
-                    axis[2].imshow(imageWithoutPrnuNpArray + imageNoise)
+                    axis[2].set_title('First image with Gaussian noise\nClipped between 0 and 255')
+                    axisImShow(axis[2], np.clip(imageWithoutPrnuNpArray + imageNoise, 0, 255))

-                    axis[3].set_title('F. i. with G. n. and PRNU')
-                    axis[3].imshow(imageWithoutPrnuNpArray + prnuNpArray + imageNoise)
+                    axis[3].set_title('Actual Gaussian noised PRNU')
+                    axisImShow(axis[3], prnuNpArray)
+
+                    axes[1][0].set_title('First image with Gaussian noise and PRNU\nClipped between 0 and 255')
+                    axisImShow(axes[1][0], np.clip(imageWithoutPrnuNpArray + prnuNpArray + imageNoise, 0, 255))
                isFirstImage = False

                #assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image'
@@ -91,10 +102,8 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
    rms = rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True)
    title = f'RMS with actual PRNU: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray):.4f}\n(normalized RMS: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True):.4f})'
    axis = axes[1]
-    axis[splitNXNIndex].set_title(f'Number of images: {len(imagesPrnuEstimateNpArray)}\n{title}')
-    axis[splitNXNIndex].imshow(cameraPrnuEstimateNpArray)
-
-axes[1][3].axis('off')
+    axis[splitNXNIndex + 1].set_title(f'PRNU estimation\nwith {len(imagesPrnuEstimateNpArray)} images')#\n{title}
+    axisImShow(axis[splitNXNIndex + 1], cameraPrnuEstimateNpArray)

 plt.tight_layout()
 plt.show()
--- a/datasets/raise/benchmark_load_part_of_images.py
+++ b/datasets/raise/benchmark_load_part_of_images.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python

-from utils import getColorChannel, Color
+from utils import getColorChannel, Color, isARawImage
 import os
 from tqdm import tqdm
 import numpy as np
@@ -19,7 +19,7 @@ class Operation(Enum):
    def __str__(self):
        return self.name.lower()

-OPERATION = Operation.LOAD_NPY
+OPERATION = Operation.SAVE
 RESOLUTION = 100
 print(f'{OPERATION = }')
 if OPERATION == Operation.LOAD_NPY:
@@ -32,7 +32,7 @@ def getNumpyFilePath(imageFilePath, color):
 for camera in tqdm(IMAGES_CAMERAS_FOLDER, 'Camera'):
    imagesCameraFolder = IMAGES_CAMERAS_FOLDER[camera]
    for file in tqdm(os.listdir(imagesCameraFolder), 'Image'):
-        if file.endswith('.NEF') or file.endswith('.ARW'):
+        if isARawImage(file):
            #print(file)
            imageFilePath = f'{imagesCameraFolder}/{file}'
            for color in Color:
--- a/datasets/raise/extract_4_color_channel_images_from_raw.py
+++ b/datasets/raise/extract_4_color_channel_images_from_raw.py
@@ -15,21 +15,22 @@ def getImageFilePath(imageIndex):

 def getImageColorChannel(imageIndex, color):
    imageFilePath = getImageFilePath(imageIndex)
-    return getColorChannel(imageFilePath, color).astype(np.int32)
+    colorChannel = getColorChannel(imageFilePath, color)
+    return colorChannel
+
+def crop(image, interestingPosition, yRange, xRange):
+    return image[interestingPosition[0] - yRange: interestingPosition[0] + yRange, interestingPosition[1] - xRange: interestingPosition[1] + xRange]

 # Could leverage crop already here.
-colorChannels = [getImageColorChannel(imageIndex, COLOR) for imageIndex in tqdm(IMAGE_INDEX_RANGE)]
+colorChannels = [crop(getImageColorChannel(imageIndex, COLOR), INTERESTING_POSITION, Y_RANGE, X_RANGE).astype(np.int32) for imageIndex in tqdm(IMAGE_INDEX_RANGE)]
 meanColorChannels = np.mean(colorChannels, axis = 0)
 estimatedPrnus = [colorChannel - meanColorChannels for colorChannel in tqdm(colorChannels)]
 estimatedPrnu = np.mean(estimatedPrnus, axis = 0)
 print(estimatedPrnu.shape)

-def crop(image, interestingPosition, yRange, xRange):
-    return image[interestingPosition[0] - yRange: interestingPosition[0] + yRange, interestingPosition[1] - xRange: interestingPosition[1] + xRange]
-
 #colorChannel = crop(multipleColorsImage, ORIGINAL_INTERESTING_POSITION, Y_RANGE, X_RANGE)
 colorChannelToDisplay = estimatedPrnu
-colorChannelToDisplay = crop(colorChannelToDisplay, INTERESTING_POSITION, Y_RANGE, X_RANGE)
+#colorChannelToDisplay = crop(colorChannelToDisplay, INTERESTING_POSITION, Y_RANGE, X_RANGE)
 print(colorChannelToDisplay.shape)
 plt.imshow(colorChannelToDisplay)
 plt.show()
--- a/datasets/raise/fft/analyze_fft_ellipses.py
+++ b/datasets/raise/fft/analyze_fft_ellipses.py
@@ -31,11 +31,13 @@ for axis in Axis:
    axisProfile = PROFILES_OF_AXES[axis]
    colorsOfAxes[axis] = plt.plot(axisProfile, label = axis)[0].get_color()

+'''
 for axis in Axis:
    for interestingValueOfAxisIndex, interestingValueOfAxis in enumerate(INTERESTING_VALUES_OF_AXES[axis]):
        linestyle = ':' if interestingValueOfAxisIndex % 2 == 0 else '--'
        label = f'{axis} local {"minimum" if interestingValueOfAxisIndex == 0 else "maximum"}' if interestingValueOfAxisIndex <= 1 else None
        plt.axvline(interestingValueOfAxis, color = colorsOfAxes[axis], alpha = 0.3, linestyle = linestyle, label = label)
+'''

 def flattenDictValues(dict_):
    return itertools.chain(*dict_.values())
@@ -43,6 +45,7 @@ def flattenDictValues(dict_):
 # TODO: How to avoid hardcoding the default color being `black`?
 MATPLOTLIB_DEFAULT_COLOR = 'black'

+'''
 allInterestingValuesOfAxes = list(set(flattenDictValues(INTERESTING_VALUES_OF_AXES)))
 ticks = list(plt.xticks()[0]) + allInterestingValuesOfAxes
 plt.xticks(ticks)
@@ -62,6 +65,7 @@ allInterestingIntensitiesOfAxes = list(flattenDictValues(INTERESTING_PROFILES_OF
 allInterestingIntensitiesOfAxesMin = min(allInterestingIntensitiesOfAxes)
 allInterestingIntensitiesOfAxesMax = max(allInterestingIntensitiesOfAxes)
 plt.ylim(allInterestingIntensitiesOfAxesMin, allInterestingIntensitiesOfAxesMax)
+'''

 plt.legend()
 plt.show()
--- a/datasets/raise/fft/generate_vignetting.py
+++ b/datasets/raise/fft/generate_vignetting.py
@@ -2,6 +2,8 @@ import numpy as np
 import matplotlib.pyplot as plt
 from tqdm import tqdm

+# TODO: seems to avoid some intermediary values resulting in visible circles
+
 A = -1.076923838846692e-06
 B = 0.0010539307456559996
 C = 42
@@ -27,9 +29,12 @@ for y in tqdm(range(SHAPE[0])):
 def checkOriginalPoint(point):
    print(f'distance = {getDistance(*point)} intensity = {image[tuple(point)]}')

-for point in [CENTER, [0, 0], [CENTER[0], int(CENTER[1] + getDistance(0, 0) / 2)]]:
-    checkOriginalPoint(point)
+#for point in [CENTER, [0, 0], [CENTER[0], int(CENTER[1] + getDistance(0, 0) / 2)]]:
+#    checkOriginalPoint(point)
+
+pad = (SHAPE[1] - SHAPE[0]) // 2
+cropImage = image[:, pad:-pad]

 #plt.imshow(image, cmap = 'grey')
-plt.imsave('vignetting.png', image * 2.55, cmap = 'grey', vmin = 0, vmax = 255)
+plt.imsave('vignetting_odd.png', image * 2.55, cmap = 'grey', vmin = 0, vmax = 255)
 plt.show()
--- a/datasets/raise/fft/image_to_fourier.py
+++ b/datasets/raise/fft/image_to_fourier.py
@@ -3,13 +3,15 @@ import numpy as np
 from scipy import fftpack
 import matplotlib.pyplot as plt

-image = np.array(Image.open('circle.png').convert('L'))# / 255
+IMAGE_FILE_NAME = 'manual_tests/horizontal_ellipse'
+image = np.array(Image.open(f'{IMAGE_FILE_NAME}.png').convert('L'))# / 255

 # fft of image
 fft1 = fftpack.fftshift(fftpack.fft2(image))

 # save the image
+#toDisplay = np.angle(fft1)
 toDisplay = np.log10(1 + abs(fft1))
 plt.imshow(toDisplay)
 plt.show()
-plt.imsave('ifft1.png', toDisplay)
+#plt.imsave(f'{IMAGE_FILE_NAME}_fft.png', toDisplay)
--- a/datasets/raise/fft/verify_dots.py
+++ b/datasets/raise/fft/verify_dots.py
@@ -0,0 +1,48 @@
+import os
+from tqdm import tqdm
+import numpy as np
+import matplotlib.pyplot as plt
+import sys
+
+sys.path.insert(0, '../')
+
+from utils import isARawImage, Color, getColorChannel, mergeSingleColorChannelImagesAccordingToBayerFilter, iterativeMean, getPearsonCorrelationDistance
+
+FOLDERS = {
+    'Rafael 23/04/24': '../rafael/230424',
+    'RAISE flat-field': '../flat-field/nef',
+    'RAISE non-flat-field': '../non-flat-field/nef',
+}
+
+def getImageMergedColorChannels(filePath):
+    imageColorChannels = {color: getColorChannel(filePath, color) for color in Color}
+    imageMergedColorChannels = mergeSingleColorChannelImagesAccordingToBayerFilter(imageColorChannels)
+    return imageMergedColorChannels
+
+def getEstimatedPrnu(folder):
+    meanImages = iterativeMean()
+    filePaths = [f'{folder}/{file}' for file in os.listdir(folder) if isARawImage(file)]
+
+    for filePath in tqdm(filePaths, 'Compute mean image'):
+        imageMergedColorChannels = getImageMergedColorChannels(filePath)
+        meanImages.add(imageMergedColorChannels)
+
+    # Is not there anything clever than this to do? See [Robust_image_source_identification_on_modern_smartphones/issues/72](https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/72).
+    estimatedPrnu = iterativeMean()
+    for filePath in tqdm(filePaths, 'Compute estimated PRNU'):
+        imageMergedColorChannels = getImageMergedColorChannels(filePath)
+        estimatedPrnu.add(imageMergedColorChannels - meanImages.mean)
+    return estimatedPrnu.mean
+
+estimatedPrnus = {folderName: getEstimatedPrnu(folder) for folderName, folder in FOLDERS.items()}
+
+# TODO: largest common crop
+
+print(f'{corr(estimatedPrnus["Rafael 23/04/24"], estimatedPrnus["Rafael 23/04/24"])=}')
+print(f'{corr(estimatedPrnus["Rafael 23/04/24"], estimatedPrnus["RAISE flat-field"])=}')
+print(f'{corr(estimatedPrnus["RAISE flat-field"], estimatedPrnus["Rafael 23/04/24"])=}')
+print(f'{corr(estimatedPrnus["Rafael 23/04/24"], estimatedPrnus["RAISE non-flat-field"])=}')
+print(f'{corr(estimatedPrnus["RAISE flat-field"], estimatedPrnus["RAISE non-flat-field"])=}')
+
+plt.imshow(estimatedPrnus['RAISE flat-field'])
+plt.show()
--- a/datasets/raise/find_consecutive_most_similar_images.py
+++ b/datasets/raise/find_consecutive_most_similar_images.py
@@ -0,0 +1,48 @@
+#!/usr/bin/python3
+
+import os
+from utils import getColorChannel, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, isARawImage
+from tqdm import tqdm
+import numpy as np
+
+folder = 'flat-field/NEF'
+
+# Section I. of *Determining Image Origin and Integrity Using Sensor Noise*.
+def dotProduct(X, Y):
+    return np.multiply(X, Y).sum()
+
+# Section I. of *Determining Image Origin and Integrity Using Sensor Noise*.
+def corr(X, Y):
+    XMinusItsMean = X - X.mean()
+    YMinusItsMean = Y - Y.mean()
+    return dotProduct(XMinusItsMean, YMinusItsMean) / (np.linalg.norm(XMinusItsMean) * np.linalg.norm(YMinusItsMean))
+
+def crop(image, yRange, xRange):
+    interestingPosition = np.array(image.shape) // 2
+    return image[interestingPosition[0] - yRange: interestingPosition[0] + yRange, interestingPosition[1] - xRange: interestingPosition[1] + xRange]
+
+lastMergedSingleColorChannelImage = None
+lastCorrelation = None
+
+# Supports both highest and lowest.
+extremeCorrelation = None
+extremeCorrelationFile = None
+
+files = [file for file in sorted(os.listdir(folder)) if isARawImage(file)]
+
+for file in tqdm(files, 'File'):
+    colorChannels = {}
+    for color in Color:
+        colorChannel = getColorChannel(f'{folder}/{file}', color)
+        colorChannel = crop(colorChannel, 200, 200)
+        colorChannels[color] = colorChannel
+    mergedSingleColorChannelImage = mergeSingleColorChannelImagesAccordingToBayerFilter(colorChannels)
+    if lastMergedSingleColorChannelImage is not None:
+        correlation = corr(mergedSingleColorChannelImage, lastMergedSingleColorChannelImage)
+        if extremeCorrelation is None or correlation < extremeCorrelation:
+            extremeCorrelation = correlation
+            extremeCorrelationFile = file
+
+    lastMergedSingleColorChannelImage = mergedSingleColorChannelImage
+
+print(f'{extremeCorrelationFile} (and previous file) have the lowest correlation with a correlation of {extremeCorrelation}')
--- a/datasets/raise/utils.py
+++ b/datasets/raise/utils.py
@@ -10,13 +10,13 @@ import builtins as __builtin__
 from scipy.ndimage import gaussian_filter
 import os
 from skimage.restoration import estimate_sigma
-from scipy import fftpack
+from scipy import fftpack, stats
 import imageio

 class Color(Enum):
    RED = auto()
    GREEN_RIGHT = auto()
-    GREEN_BOTTOM = auto()
+    GREEN_LEFT = auto()
    BLUE = auto()

    def __str__(self):
@@ -44,7 +44,7 @@ class Distance(Enum):
        return self.name.replace('_', '').title()

 def getPearsonCorrelationDistance(firstImage, secondImage):
-    pearsonCorrelation = scipy.stats.pearsonr(firstImage.flatten(), secondImage.flatten()).statistic
+    pearsonCorrelation = stats.pearsonr(firstImage.flatten(), secondImage.flatten()).statistic
    if pearsonCorrelation < 0:
        print('Negative Pearson correlation, investigate further!')
        exit(1)
@@ -117,29 +117,44 @@ RAW_IMAGE_FILE_EXTENSIONS = [
    'nef',
 ]

+SUPPORTED_RAW_PATTERNS = [
+    # RG
+    # GB
+    [[0, 1],
+     [3, 2]],
+    # GB
+    # RG
+    [[3, 2],
+     [0, 1]],
+]
+
 def getRawColorChannel(raw, color):
    colorDesc = raw.color_desc.decode('ascii')
    assert colorDesc == 'RGBG'
-    assert np.array_equal(raw.raw_pattern, np.array([[0, 1], [3, 2]], dtype = np.uint8))
-    # RG
-    # GB
+    assert any(np.array_equal(raw.raw_pattern, np.array(supportedRawPattern, dtype = np.uint8)) for supportedRawPattern in SUPPORTED_RAW_PATTERNS)
    rawImageVisible = raw.raw_image_visible.copy()

-    redRawImageVisible = rawImageVisible[::2, ::2]
-    greenRightRawImageVisible = rawImageVisible[::2, 1::2]
+    topLeftRawImageVisible = rawImageVisible[::2, ::2]
+    topRightRawImageVisible = rawImageVisible[::2, 1::2]

-    greenBottomRawImageVisible = rawImageVisible[1::2, ::2]
-    blueRawImageVisible = rawImageVisible[1::2, 1::2]
+    bottomLeftRawImageVisible = rawImageVisible[1::2, ::2]
+    bottomRightRawImageVisible = rawImageVisible[1::2, 1::2]

-    match color:
-        case Color.RED:
-            imageNpArray = redRawImageVisible
-        case Color.GREEN_RIGHT:
-            imageNpArray = greenRightRawImageVisible
-        case Color.GREEN_BOTTOM:
-            imageNpArray = greenBottomRawImageVisible
-        case Color.BLUE:
-            imageNpArray = blueRawImageVisible
+    if np.array_equal(raw.raw_pattern, np.array(SUPPORTED_RAW_PATTERNS[0], dtype = np.uint8)):
+        imageNpArrays = {
+            Color.RED: topLeftRawImageVisible,
+            Color.GREEN_RIGHT: topRightRawImageVisible,
+            Color.GREEN_LEFT: bottomLeftRawImageVisible,
+            Color.BLUE: bottomRightRawImageVisible,
+        }
+    else: # elif np.array_equal(raw.raw_pattern, np.array(SUPPORTED_RAW_PATTERNS[1], dtype = np.uint8)):
+        imageNpArrays = {
+            Color.GREEN_LEFT: topLeftRawImageVisible,
+            Color.BLUE: topRightRawImageVisible,
+            Color.RED: bottomLeftRawImageVisible,
+            Color.GREEN_RIGHT: bottomRightRawImageVisible,
+        }
+    imageNpArray = imageNpArrays[color]
    return imageNpArray

 def isARawImage(imageFilePath):
@@ -148,7 +163,7 @@ def isARawImage(imageFilePath):
 def getColorChannel(imageFilePath, color):
    if isARawImage(imageFilePath):
        numpyFilePath = f'{imageFilePath}.{color}.npy'
-        if False and os.path.isfile(numpyFilePath):
+        if os.path.isfile(numpyFilePath):
            imageNpArray = np.load(numpyFilePath)
        else:
            with rawpy.imread(imageFilePath) as raw:
@@ -161,7 +176,6 @@ def getColorChannel(imageFilePath, color):
 def escapeFilePath(filePath):
    return filePath.replace('/', '_')

-
 def getNewIndex(index, offset):
    newIndex = (index - offset) * 2 + offset
    return newIndex
@@ -179,7 +193,7 @@ def mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages
    '''
    multipleColorsImage[::2,::2] = singleColorChannelImages[Color.RED]
    multipleColorsImage[1::2,::2] = singleColorChannelImages[Color.GREEN_RIGHT]
-    multipleColorsImage[::2,1::2] = singleColorChannelImages[Color.GREEN_BOTTOM]
+    multipleColorsImage[::2,1::2] = singleColorChannelImages[Color.GREEN_LEFT]
    multipleColorsImage[1::2,1::2] = singleColorChannelImages[Color.BLUE]
    return multipleColorsImage

--- a/documentation/slides/slides.tex
+++ b/documentation/slides/slides.tex
@@ -1,5 +1,15 @@
 \documentclass{beamer}

+% Only show at bottom right the current page number over the total number of pages.
+\setbeamertemplate{navigation symbols}
+{
+    \usebeamerfont{footline}
+    \usebeamercolor[fg]{footline}
+    \hspace{1em}
+    \insertframenumber/\inserttotalframenumber
+}
+
+% To avoid warning `Token not allowed in a PDF string (Unicode):(hyperref) removing `\\'`
 \pdfstringdefDisableCommands{%
  \def\\{}%
 }
Author	SHA1	Message	Date
Benjamin Loison	57fc05ed2c	Add `articles/ipol_style_guide/`	2024-07-24 00:23:39 +02:00
Benjamin Loison	66c1a50d3b	Add `articles/Hierarchical Data Format - Wikipedia/`	2024-07-24 00:23:01 +02:00
Benjamin Loison	19f8e010e0	Add debugging	2024-07-24 00:22:08 +02:00
Benjamin Loison	7386290e87	Update annotations	2024-07-24 00:21:09 +02:00
Benjamin Loison	025f8d0bee	Remove debugging	2024-07-24 00:20:45 +02:00
Benjamin Loison	3a4100b779	Use `np.clip`	2024-07-04 04:18:13 +02:00
Benjamin Loison	98426db9dd	Using matplotlib clipping	2024-07-04 04:11:12 +02:00
Benjamin Loison	d24c883a92	Restore clipping `6f961a46cf`	2024-07-04 04:08:36 +02:00
Benjamin Loison	5bc0337dc8	Forcing color map	2024-07-04 04:08:10 +02:00
Benjamin Loison	9d20940cb1	cmap try	2024-07-04 04:03:54 +02:00
Benjamin Loison	6f961a46cf	Add clipping	2024-07-04 03:58:30 +02:00
Benjamin Loison	a17ec28cc3	Add color scales	2024-07-04 03:36:36 +02:00
Benjamin Loison	dd7a6892e5	Add `articles/Discrete cosine transform - Wikipedia/`	2024-06-28 13:55:24 +02:00
Benjamin Loison	ad5bb767c2	Add `articles/JPEG - Wikipedia/`	2024-06-28 12:57:21 +02:00
Benjamin Loison	0fca7c84a0	Support Nikon D90 `.nef`s	2024-06-17 13:48:21 +00:00
Benjamin Loison	715b5296c9	Compare PRNUs	2024-06-11 17:54:29 +02:00
Benjamin Loison	c2cf139261	Correct `pearsonr` `import`	2024-06-11 17:53:34 +02:00
Benjamin Loison	aa5c210539	Comment `checkOriginalPoint`	2024-06-11 17:52:50 +02:00
Benjamin Loison	2bfedf54ad	Remove `INTERESTING_VALUES_OF_AXES` usage	2024-06-11 17:51:57 +02:00
Benjamin Loison	a9861a761f	Use an intermediary variable in `getImageColorChannel`	2024-06-11 17:35:26 +02:00
Benjamin Loison	0c7108b3ec	Make `benchmark_load_part_of_images.py` production ready	2024-06-11 16:41:43 +02:00
Benjamin Loison	959fb43dd0	Update annotations of `Determining Image Origin and Integrity Using Sensor Noise`	2024-06-11 16:41:23 +02:00
Benjamin Loison	0d22c36e96	Update annotations of `On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation`	2024-06-11 16:41:02 +02:00
Benjamin Loison	cb65dbb943	Clean bottom right controls	2024-06-10 15:47:07 +02:00
Benjamin Loison	2c799f12f7	Add comment about purpose of `\pdfstringdefDisableCommands`	2024-06-10 15:46:48 +02:00
Benjamin Loison	115bab4b4c	Add and use `getEstimatedPrnu`	2024-06-05 16:47:24 +02:00
Benjamin Loison	31da324702	Compute mean PRNU estimate from scratch	2024-06-05 16:41:44 +02:00
Benjamin Loison	be83fcf154	Add PRNU estimation computation	2024-06-05 16:27:40 +02:00
Benjamin Loison	38eca8e65c	Add `verify_dots.py`	2024-06-05 16:23:55 +02:00
Benjamin Loison	fbd7cb01e1	Support lowest correlation in `find_consecutive_most_similar_images.py`	2024-05-31 16:05:36 +02:00
Benjamin Loison	7f7e78cafb	Small changes	2024-05-31 14:07:51 +02:00
Benjamin Loison	4eb8841ebf	Clean `find_consecutive_most_similar_images.py`	2024-05-31 12:12:14 +02:00
Benjamin Loison	0dd21ad34d	Add `find_consecutive_most_similar_images.py` Using: - `dd80ba940a/utils.py (L53-L55)` - `dd80ba940a/chen.py (L81-L89)`	2024-05-31 12:10:37 +02:00
Benjamin Loison	93a3078743	Update annotations of `articles/Determining Image Origin and Integrity Using Sensor Noise/`	2024-05-30 14:54:54 +02:00
Benjamin Loison	a9336958ff	Add `articles/Expected value - Wikipedia/`	2024-05-30 14:54:41 +02:00
Benjamin Loison	a524d75389	Update annotations of `articles/`	2024-05-30 12:34:32 +02:00
Benjamin Loison	ec9f6e7aca	Make `articles/Determining Image Origin and Integrity Using Sensor Noise/` source permanent	2024-05-29 16:25:53 +02:00
Benjamin Loison	fcb9e84d28	Update Wiener related annotations	2024-05-29 15:57:30 +02:00
Benjamin Loison	30f44e3e47	Update annotations of 2 articles Commit to illustrate [Benjamin-Loison/xournalpp/issues/33](https://github.com/Benjamin-Loison/xournalpp/issues/33).	2024-05-29 15:27:59 +02:00
Benjamin Loison	12c8da4f11	Add `articles/Prior knowledge for pattern recognition - Wikipedia/`	2024-05-29 14:48:31 +02:00
Benjamin Loison	82ed226346	Add `articles/Color filter array - Wikipedia/`	2024-05-28 16:54:41 +02:00
Benjamin Loison	81d7a03f1b	Add `articles/Fisher information - Wikipedia/`	2024-05-28 16:36:10 +02:00
Benjamin Loison	37f35db747	Add `articles/Cramér–Rao bound - Wikipedia/`	2024-05-28 16:33:51 +02:00
Benjamin Loison	f9c4ea6387	Add `articles/Minimum-variance unbiased estimator - Wikipedia/`	2024-05-28 16:28:27 +02:00
Benjamin Loison	c80d37f464	Update annotations of `articles/Determining Image Origin and Integrity Using Sensor Noise`	2024-05-28 16:18:29 +02:00
Benjamin Loison	8f48ac775b	Add forgotten `articles/Depth of field - Wikipedia/`	2024-05-28 15:32:04 +02:00
Benjamin Loison	fa67029adf	Add `articles/Color balance - Wikipedia/`	2024-05-28 15:31:44 +02:00
Benjamin Loison	0968863ba3	Update annotations of `articles/Type I and type II errors - Wikipedia/`	2024-05-28 14:54:10 +02:00
Benjamin Loison	7fc37b91d6	Add `articles/Neyman–Pearson lemma - Wikipedia/`	2024-05-28 14:47:06 +02:00
Benjamin Loison	5b33ceb886	Update annotations of `articles/preprint-PRNU_WIFS-3/`	2024-05-28 13:32:39 +02:00
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				`https://en.wikipedia.org/w/index.php?title=Color_balance&oldid=1193262662`
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				`https://web.archive.org/web/20220911052242/https://tools.ipol.im/wiki/ref/manuscript_guidelines/ipol_style_guide.pdf`