diff --git a/articles/Gradient - Wikipedia/Gradient - Wikipedia.xopp.xml b/articles/Gradient - Wikipedia/Gradient - Wikipedia.xopp.xml
index 8c13dca..a438319 100644
--- a/articles/Gradient - Wikipedia/Gradient - Wikipedia.xopp.xml
+++ b/articles/Gradient - Wikipedia/Gradient - Wikipedia.xopp.xml
@@ -3,7 +3,15 @@
Xournal++ document - see https://github.com/xournalpp/xournalpp
-
+
+ 49.51098633 87.97123718 138.28824781 87.97123718
+ 167.39846802 123.17959595 303.20812975 123.17959595
+ 46.68045044 136.66586304 306.47302679 136.66586304
+ 46.00357056 152.91329956 96.58616660 152.91329956
+ 273.35638428 152.10409546 302.25897217 153.47811890
+ 44.52737427 165.98574829 306.01924979 165.98574829
+ 43.13754272 180.75143433 119.10676747 180.75143433
+
diff --git a/articles/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation.xopp.xml b/articles/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation.xopp.xml
index 519ce53..69ea844 100644
--- a/articles/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation.xopp.xml
+++ b/articles/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation/On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation.xopp.xml
@@ -679,10 +679,11 @@ and considered here?
471.15082366 735.30524325 547.59591431 735.30524325
i.e. D8
481.66235907 694.22884332 498.80405145 693.54845309
- 1
372.53639432 342.03797193 440.14635521 342.03797193
370.53286329 236.46567101 320.15005085 239.92584728
fixed for a camera in all conditions?
+ method 1
+
@@ -867,7 +868,6 @@ what happens if apply many times the filter? Get a single color image or only ed
i.e. PRNU signal
3
- 2
4
567.97026449 248.20981400 572.87123772 252.48766610
572.87123772 252.48766610 580.53180769 237.68657327
@@ -950,6 +950,9 @@ values, is it?
511.44765027 721.26582403 564.96356814 721.26582403
316.02800017 730.89063950 469.74763762 730.89063950
1
+ 2
+ 3
+ 4
@@ -1001,6 +1004,136 @@ values, is it?
237.84039133 358.89389861 206.71850860 242.74545101
46.58963278 382.50527023 202.56231204 382.50527023
136.51804152 392.15665604 210.57548036 387.27930856
+ 2
+ 76.84186218 403.18324877 76.84186218 403.18324877
+ 99.44224092 402.23410746 167.54000794 402.23410746
+ 245.47029898 402.09162091 296.35317123 402.09162091
+ 49.07982924 414.26806860 96.85503183 414.26806860
+ 174.35205341 412.61054299 294.68741343 412.61054299
+ 49.17852944 425.60038529 243.41325935 425.60038529
+ ?
+ 83.88241632 436.25593613 129.82894348 436.25593613
+ 222.64784479 437.01733447 257.32877140 437.01733447
+ 48.10488202 445.99353438 48.10488202 469.74916235 288.98718215 469.74916235 288.98718215 445.99353438 48.10488202 445.99353438
+ 96.67739131 477.84746422 296.47631090 477.84746422
+ 48.49567964 496.13273976 82.59162509 496.13273976
+ 102.75629699 496.36044683 162.86191595 496.36044683
+ 250.68924182 495.95829552 268.05641640 495.95829552
+ 288.20029455 496.47925347 297.10701532 496.47925347
+ 46.43172813 507.37128674 77.85353926 508.67483506
+ 106.84210994 501.29532126 141.30300883 501.29532126
+ 217.19893319 502.52884762 281.37355428 502.52884762
+ 47.67536425 519.43351902 63.36524961 519.43351902
+ 205.81710807 242.65357749 65.20331250 511.97930156
+ 71.21794091 510.09639843 69.96638887 516.48354999
+ 96.04959108 512.41056083 137.65027490 512.41056083
+ 178.81011811 514.56663103 293.20273510 514.56663103
+ 49.15736001 524.63771564 63.80203650 526.02015175
+ 88.28307930 525.07987626 223.17957213 525.07987626
+ 162.93462928 531.18612578 166.01962127 535.50102543
+ 166.01962127 535.50102543 169.57278333 526.45516543
+
+ 282.04044295 508.01858275 285.05604149 513.24175263
+ 285.12163530 513.20332882 288.64857023 500.04062848
+ 178.41391885 554.21259303 197.20623956 554.21259303
+ 210.02175058 542.62035386 283.96825581 542.62035386
+ 202.25153063 547.88757351 295.30670531 547.88757351
+ 49.32541317 560.53635965 294.72733417 560.53635965
+ 48.90584937 571.43159705 137.34801784 571.43159705
+ https://github.com/Benjamin-Loison/Anisotropic-diffusion-for-denoising-and-edge-detection/releases
+ unclear
+ 171.21302600 573.41032280 211.75072971 573.41032280
+ 237.92023741 571.85788869 295.86145032 571.85788869
+ 46.91425574 583.56508484 131.72259356 583.56508484
+ ?
+ 168.20310284 584.11182559 196.21031508 584.11182559
+ 258.58680090 584.04945568 275.33833952 582.88921057
+ 47.10245960 595.60017784 114.74259490 595.60017784
+ 165.46905583 598.65122326 298.64041918 598.65122326
+ 295.03641878 612.43390949 46.07445931 612.43390949
+ 91.74851613 617.45086001 294.76942821 617.45086001
+ 47.53472823 630.20950576 232.46200341 630.20950576
+ 273.62459149 635.66422988 296.63970651 633.41798629
+ 46.69934643 645.88235728 142.15067230 645.88235728
+ 156.21009323 648.31531586 270.65515205 648.31531586
+ 189.00521877 653.15239937 293.08714811 653.15239937
+ 47.05890879 664.43656482 131.08309882 664.43656482
+ 136.92783626 670.87290279 203.38071112 670.87290279
+ 95.07336101 683.36998622 95.07336101 703.34167614 256.37301232 703.34167614 256.37301232 683.36998622 95.07336101 683.36998622
+ 48.98520624 711.66282458 81.12531565 711.66282458
+ 106.23615914 710.50875811 298.05243590 710.50875811
+ 83.50512413 729.90773983 228.26677892 729.90773983
+ 268.97849651 723.44267120 295.62419385 723.44267120
+ 316.71952257 58.74026193 359.12877601 58.74026193
+ 412.96511696 59.80409455 530.73510330 59.80409455
+ 399.11371055 60.59818675 399.11371055 60.59818675
+ 313.40427175 77.63994111 445.55965406 77.63994111
+ 475.18210668 73.14690472 500.46574272 71.34604648
+ 520.62873011 70.95813961 547.06876676 72.44193936
+ 436.63993882 84.22816200 558.60744929 84.22816200
+ 314.91522318 95.34286115 344.67805823 95.34286115
+ 460.50410158 95.07233981 504.44148623 95.07233981
+ 550.55914763 94.72192527 561.33585816 94.72192527
+ 315.18190003 107.91745216 445.89716587 107.91745216
+ 415.38717711 112.17717635 417.97873788 114.76873711
+ 417.97873788 114.92445593 422.21614813 107.58504609
+ 411.79395626 117.61849626 475.42345742 117.61849626
+ 420.15828651 135.26224727 564.46020303 135.26224727
+ 315.08396863 146.10754068 384.08694673 146.10754068
+ 411.07016323 146.48127958 565.13947989 146.48127958
+ 314.60577643 157.34742589 352.47754624 157.34742589
+ 314.68805528 136.02513735 361.93686418 136.02513735
+ 421.31660937 122.11803739 424.48302549 125.28445350
+ 424.36453116 125.39867068 429.34866624 118.81323876
+ 416.76655789 180.06780275 431.42158160 180.06780275
+ 485.05330359 181.02707052 560.75154816 181.02707052
+ 313.99052145 191.22921927 432.03511528 191.22921927
+ 492.43097412 187.56077351 557.33234280 187.56077351
+ 313.95986855 198.47259024 521.21137689 198.47259024
+ see the following
+ 561.34993172 197.87348554 561.34993172 197.87348554
+ 315.17833015 209.84917781 563.04623783 209.84917781
+ 316.33795740 222.16066184 543.08113380 222.16066184
+ 414.96260175 232.74195581 478.11042516 232.74195581
+ 489.29229502 236.87529254 541.96961410 236.87529254
+ 314.97622006 250.17719497 562.46093511 250.17719497
+ 476.85174611 263.36168613 315.35057521 263.36168613
+ 505.71809931 257.55919054 561.17232043 257.55919054
+ 331.14313945 267.49191638 470.77428508 267.49191638
+ 493.68849844 267.91834533 562.54366699 267.91834533
+ 371.98008753 278.96030819 559.50772790 278.96030819
+ 316.66830854 291.43961089 466.36481632 291.43961089
+ insuffisant
+ 545.28839242 290.17894576 562.32203867 290.17894576
+ 316.06805392 302.05795951 353.19542093 302.05795951
+ 411.00473832 302.69830662 539.12003920 302.69830662
+ 312.58924084 318.67406583 435.84710602 318.67406583
+ 492.48514017 312.61941319 561.87767326 312.61941319
+ 314.19407323 327.13382170 359.04993060 327.13382170
+ 367.53287603 325.65776241 450.83692225 325.65776241
+ 459.17950472 329.29097278 563.62769923 329.29097278
+ 318.10786238 336.75504124 477.39926401 336.75504124
+ unclear noise-block link
+ 314.47194028 354.11078194 455.82909866 354.11078194
+ compared to something?
+ 483.51485723 348.76262214 563.67254052 348.76262214
+ 315.95000762 360.20868505 533.11589460 360.20868505
+ 433.43888981 370.63206334 560.59662648 370.63206334
+ 315.50181614 383.55018659 504.85861462 383.55018659
+ 567.69298943 365.18646987 542.62754640 365.18646987
+ 406.57211527 377.30420214 314.67123868 377.30420214
+ precision?
+ 425.55529187 181.01957034 527.94193685 409.07841084
+ 369.12713609 472.77336493 373.64759877 476.40916689
+ 373.64759877 476.40916689 379.47765795 467.39067342
+ 397.34243254 449.96497682 566.96742242 449.96497682
+ 565.89912506 461.26742073 509.20040554 461.26742073
+ 485.14375198 460.93792773 316.11338465 460.93792773
+ 316.90642635 472.38622182 466.14785910 472.38622182
+ 429.95485921 478.10544311 563.18128115 478.10544311
+ 313.62529534 490.53612336 539.85061358 490.53612336
+ 329.33468664 512.65494122 359.71311722 512.65494122
+ 314.65973653 501.31010499 336.66679790 501.31010499
@@ -1011,6 +1144,23 @@ values, is it?
319.91997570 586.40761369 441.19501087 586.40761369
8
9
+ 107.83438382 468.14324860 152.60021932 468.14324860
+ 193.07603112 469.53466056 206.79421949 469.53466056
+ 239.76476660 469.86072098 289.99295688 469.86072098
+ 138.16140120 482.24008421 207.11233037 482.24008421
+
+ 175.06156188 494.15572356 175.06156188 494.15572356
+ 261.45833975 492.28368227 261.45833975 492.28368227
+ 78.35414868 492.82731654 101.22964579 492.82731654
+ 82.10912968 510.27196096 99.99695740 510.27196096
+ 169.73825393 510.10472584 181.85670111 510.10472584
+ 256.99544137 509.82785424 264.36316184 509.82785424
+ 254.54736230 589.86087597 266.97243359 589.86087597
+ 171.61494636 589.60030911 179.56596670 589.60030911
+ 80.76968693 589.56725340 99.26425718 589.56725340
+ unclear what we want high or low
+as far as I understand I would say
+respectively high and low