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Author SHA1 Message Date
Benjamin Loison
a5cf1083bd
Before optimizing extract_4_color_channel_images_from_raw.py 2024-05-23 16:55:11 +02:00
Benjamin Loison
2144babd8f
Subtract an image to the other 2024-05-23 14:53:16 +02:00
Benjamin Loison
6ad8483990
Add and use COLOR 2024-05-23 14:48:39 +02:00
Benjamin Loison
cc05f5c210
Add and use getImageColorChannel 2024-05-23 14:48:11 +02:00
Benjamin Loison
3c983b0690
Add and use getImageFilePath 2024-05-23 14:47:25 +02:00
Benjamin Loison
95d4d841c4
Use crop for multipleColorsImage to verify ORIGINAL_INTERESTING_POSITION 2024-05-23 14:45:04 +02:00
Benjamin Loison
24b6971632
Add extract_4_color_channel_images_from_raw.py 2024-05-23 12:21:04 +02:00
Benjamin Loison
7db83a10c9
Update uncommited 2024-05-23 12:17:54 +02:00
Benjamin Loison
40a88d25c7
Mention the related issues faced in remove_period_patterns.py 2024-05-23 09:05:34 +02:00
Benjamin Loison
7970c8d601
Restore what should work 2024-05-23 02:34:40 +02:00
Benjamin Loison
fbdb2393a9
Restore what should work 2024-05-23 02:33:20 +02:00
Benjamin Loison
81a994ed65
CLean remove_period_patterns.py and add debugging as unable to find back process to get wanted result 2024-05-23 01:55:25 +02:00
Benjamin Loison
0f5b9dcdaf
Make generate_vignetting.py ready 2024-05-23 00:49:01 +02:00
Benjamin Loison
6a2b6386c9
Add generate_vignetting.py 2024-05-22 21:36:25 +02:00
Benjamin Loison
92026d7134
Use plt.suptitle for the actual title in analyze_fft_ellipses.py 2024-05-22 20:30:48 +02:00
Benjamin Loison
a39a88c992
Finish cleaning analyze_fft_ellipses.py 2024-05-22 20:22:44 +02:00
Benjamin Loison
85bc972c54
Clean completely analyze_fft_ellipses.py 2024-05-22 20:13:20 +02:00
Benjamin Loison
80ac1f1bf3
Clean analyze_fft_ellipses.py 2024-05-22 19:53:20 +02:00
Benjamin Loison
ffd71ce5b8
Add analyze_fft_ellipses.py 2024-05-22 19:19:09 +02:00
Benjamin Loison
7a002f8b0d
Add 1 to np.log10 argument 2024-05-22 16:43:17 +02:00
Benjamin Loison
4d9e8bead1
Update annotations of articles/Noise reduction - Wikipedia/ 2024-05-22 16:21:26 +02:00
Benjamin Loison
417f9d0e1b
Add articles/Shrinkage Fields (image restoration) - Wikipedia 2024-05-22 16:21:03 +02:00
Benjamin Loison
484ef23aa2
Add articles/Random field - Wikipedia/ 2024-05-22 16:12:42 +02:00
Benjamin Loison
3a0cd67724
Add articles/Block-matching and 3D filtering - Wikipedia/ 2024-05-22 16:08:44 +02:00
Benjamin Loison
ee550d55aa
Add articles/Mathematical morphology - Wikipedia/ 2024-05-22 14:30:37 +02:00
Benjamin Loison
654b524dcd
Add articles/Salt-and-pepper noise - Wikipedia/ 2024-05-22 14:26:53 +02:00
Benjamin Loison
2dca1757bd
Add articles/The Adobe Hidden Feature and its Impact on Sensor Attribution/ 2024-05-21 17:11:31 +02:00
Benjamin Loison
31d921fbfa
Add articles/Adobe Photoshop - Wikipedia/ 2024-05-21 16:37:24 +02:00
Benjamin Loison
c37df7d766
Add articles/Adobe Lightroom - Wikipedia/ 2024-05-21 16:36:52 +02:00
Benjamin Loison
a65dca67b5
Add articles/Dither - Wikipedia/ 2024-05-21 16:28:13 +02:00
Benjamin Loison
454db74c7e
Add articles/Colour banding - Wikipedia/ 2024-05-21 16:22:39 +02:00
Benjamin Loison
7688974a0d
Add articles/A leak in PRNU based source identification. Questioning fingerprint uniqueness/ 2024-05-21 15:48:47 +02:00
Benjamin Loison
b7a338f1b5
Add articles/Constant false alarm rate - Wikipedia/ 2024-05-21 15:32:18 +02:00
Benjamin Loison
403090891e
Add articles/Clutter (radar) - Wikipedia/ 2024-05-21 15:26:34 +02:00
Benjamin Loison
d11cbb2d5d
Add articles/Maximum likelihood estimation - Wikipedia/ 2024-05-21 15:22:40 +02:00
Benjamin Loison
95232e2679
Add articles/Confusion matrix - Wikipedia/ 2024-05-21 15:19:58 +02:00
Benjamin Loison
98c3358acb
Add articles/Type I and type II errors - Wikipedia/ 2024-05-21 15:15:39 +02:00
Benjamin Loison
c914a57158
Add articles/Signal-to-noise ratio - Wikipedia/ 2024-05-21 15:11:36 +02:00
Benjamin Loison
53c6d4859c
Add articles/Detection theory - Wikipedia/ 2024-05-21 15:08:13 +02:00
Benjamin Loison
1bb508cae2
Add articles/Bokeh - Wikipedia/ 2024-05-21 13:46:59 +02:00
Benjamin Loison
1c3291fc96
Add articles/False alarm - Wikipedia/ 2024-05-21 13:41:29 +02:00
Benjamin Loison
f3241b242b
Add articles/Checking_PRNU_usability_on_modern_devices/ 2024-05-21 13:37:59 +02:00
Benjamin Loison
b9c1979270
Use permanent URL 2024-05-21 13:29:16 +02:00
Benjamin Loison
006baaf5dd
Add articles/A comparison study of CNN denoisers on PRNU extraction/ 2024-05-21 11:56:13 +02:00
Benjamin Loison
78d319ac5b
Add articles/Blind PRNUBased Image Clusteringfor Source Identification2017/ 2024-05-21 11:47:25 +02:00
Benjamin Loison
90df10480d
Add articles/Digital Camera Identification from Sensor Pattern Noise/ 2024-05-21 11:33:09 +02:00
Benjamin Loison
822ea266f2
Add articles/Distinguishing Digital Image Origin via Higher Order Statistics - Large_scale_test_of_sensor_fingerprint_camera_iden/ 2024-05-21 11:25:46 +02:00
Benjamin Loison
7979b9474b
Add articles/Determining Image Origin and Integrity Using Sensor Noise 2024-05-21 11:17:42 +02:00
Benjamin Loison
c64b141adf
Update annotations of articles/Wu_ICIP_2012/ 2024-05-21 11:01:01 +02:00
Benjamin Loison
679c63896d
Add articles/A context-adaptive SPN predictor for trustworthy source camera identification/ 2024-05-21 10:52:48 +02:00
Benjamin Loison
036c135e8a
Update annotations 2024-05-21 10:39:42 +02:00
Benjamin Loison
c26337e418
Add articles/IPOL MLBriefs Workshop IPOL MLBriefs 4/ 2024-05-21 00:43:45 +02:00
Benjamin Loison
307548502e
Add articles/Noise reduction - Wikipedia/ 2024-05-17 13:53:57 +02:00
64 changed files with 2640 additions and 25 deletions
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articles/A comparison study of CNN denoisers on PRNU extraction/A comparison study of CNN denoisers on PRNU extraction.pdf.txt Normal file
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https://web.archive.org/web/20240521094929/https://arxiv.org/pdf/2112.02858

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articles/A context-adaptive SPN predictor for trustworthy source camera identification/A context-adaptive SPN predictor for trustworthy.pdf.txt Normal file
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https://web.archive.org/web/20240521084451/https://link.springer.com/content/pdf/10.1186/1687-5281-2014-19.pdf

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articles/A leak in PRNU based source identification. Questioning fingerprint uniqueness/A leak in PRNU based source identification. Questioning fingerprint uniqueness.pdf.txt Normal file
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https://en.wikipedia.org/w/index.php?title=Random_field&oldid=1211044820

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35
datasets/raise/extract_4_color_channel_images_from_raw.py Normal file
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@ -0,0 +1,35 @@
import numpy as np
import matplotlib.pyplot as plt
from utils import Color, getColorChannel
from tqdm import tqdm
IMAGE_INDEX_RANGE = range(3294, 3553)
Y_RANGE = 5
X_RANGE = 25
ORIGINAL_INTERESTING_POSITION = [5752, 1695][::-1]
INTERESTING_POSITION = np.array(ORIGINAL_INTERESTING_POSITION) // 2
COLOR = Color.RED
def getImageFilePath(imageIndex):
return f'../rafael/240424/photos/DSC0{imageIndex}.ARW'
def getImageColorChannel(imageIndex, color):
imageFilePath = getImageFilePath(imageIndex)
return getColorChannel(imageFilePath, color).astype(np.int32)
# Could leverage crop already here.
colorChannels = [getImageColorChannel(imageIndex, COLOR) 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)
print(colorChannelToDisplay.shape)
plt.imshow(colorChannelToDisplay)
plt.show()

67
datasets/raise/fft/analyze_fft_ellipses.py Normal file
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@ -0,0 +1,67 @@
import matplotlib.pyplot as plt
from enum import Enum, auto
import itertools
class Axis(Enum):
ABSCISSA = auto()
ORDINATE = auto()
def __str__(self):
return self.name.title()
INTERESTING_VALUES_OF_AXES = {
Axis.ABSCISSA: [0, 30, 60, 84, 108, 132, 160, 179],
Axis.ORDINATE: [0, 25, 40, 55, 70, 85, 100, 117, 135, 150, 167, 182],
}
halfHeight, halfWidth = np.array(secondImage.shape) // 2
PROFILES_OF_AXES = {
Axis.ABSCISSA: secondImage[halfHeight , halfWidth:],
Axis.ORDINATE: secondImage[halfHeight:, halfWidth ],
}
colorsOfAxes = {}
for axis in Axis:
plt.suptitle('Profiles of Fourier transform axes')
plt.title('Local extremas have been listed manually from the Fourier transform visualization')
plt.xlabel('Fourier transform axis')
plt.ylabel('Fourier transform logarithmic intensity')
height, width = secondImage.shape
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())
# 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)
for tickIndex, tick in enumerate(ticks):
for axis in Axis:
if tick in INTERESTING_VALUES_OF_AXES[axis]:
pltTick = plt.xticks()[1][tickIndex]
pltTick.set_color(MATPLOTLIB_DEFAULT_COLOR if axis == list(Axis)[-1] and pltTick.get_color() != MATPLOTLIB_DEFAULT_COLOR else colorsOfAxes[axis])
# TODO: Changing tick color would be nice too, see https://stackoverflow.com/questions/49997934/change-color-of-specific-ticks-at-plot-with-matplotlib#comment108290728_49998338
allInterestingValuesOfAxesMin = min(allInterestingValuesOfAxes)
allInterestingValuesOfAxesMax = max(allInterestingValuesOfAxes)
plt.xlim(allInterestingValuesOfAxesMin, allInterestingValuesOfAxesMax)
INTERESTING_PROFILES_OF_AXES = {axis: PROFILES_OF_AXES[axis][allInterestingValuesOfAxesMin:allInterestingValuesOfAxesMax + 1] for axis in Axis}
allInterestingIntensitiesOfAxes = list(flattenDictValues(INTERESTING_PROFILES_OF_AXES))
allInterestingIntensitiesOfAxesMin = min(allInterestingIntensitiesOfAxes)
allInterestingIntensitiesOfAxesMax = max(allInterestingIntensitiesOfAxes)
plt.ylim(allInterestingIntensitiesOfAxesMin, allInterestingIntensitiesOfAxesMax)
plt.legend()
plt.show()

35
datasets/raise/fft/generate_vignetting.py Normal file
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@ -0,0 +1,35 @@
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
A = -1.076923838846692e-06
B = 0.0010539307456559996
C = 42
SHAPE = (4_000, 6_000)
CENTER = np.array(SHAPE) // 2
# `dtype` by default is `float64` as wanted.
image = np.empty(SHAPE)
def getDistance(y, x):
d = ((y - CENTER[0]) ** 2 + (x - CENTER[1]) ** 2) ** 0.5
return d
def vignetting(y, x):
d = getDistance(y, x)
return A * (d ** 2) + B * d + C
for y in tqdm(range(SHAPE[0])):
for x in range(SHAPE[1]):
image[y, x] = vignetting(y, x)
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)
#plt.imshow(image, cmap = 'grey')
plt.imsave('vignetting.png', image * 2.55, cmap = 'grey', vmin = 0, vmax = 255)
plt.show()

2
datasets/raise/fft/image_to_fourier.py
View File

@ -9,7 +9,7 @@ image = np.array(Image.open('circle.png').convert('L'))# / 255
fft1 = fftpack.fftshift(fftpack.fft2(image))
# save the image
toDisplay = np.log10(abs(fft1))
toDisplay = np.log10(1 + abs(fft1))
plt.imshow(toDisplay)
plt.show()
plt.imsave('ifft1.png', toDisplay)

53
datasets/raise/fft/remove_period_patterns.py
View File

@ -10,7 +10,7 @@ sys.path.insert(0, '../')
from utils import Color, mergeSingleColorChannelImagesAccordingToBayerFilter
import matplotlib.pyplot as plt
PREFIX = 'mean_rafael_230424_mean_'
PREFIX = 'means/mean_rafael_230424_mean_'
X_STDDEV = 2
def getImageByColor(color):
@ -24,45 +24,44 @@ image = multipleColorsImage
fft1 = fftpack.fftshift(fftpack.fft2(image))
originalFft1 = fft1.copy()
def removePeriodicPatterns(fft1Part):
# This example is intended to demonstrate how astropy.convolve and
# scipy.convolve handle missing data, so we start by setting the brightest
# pixels to NaN to simulate a "saturated" data set
# Unclear why the Gaussian kernel usage does not work as expected, even when just consider the *real* part.
# See [Benjamin_Loison/astropy/issues/2](https://codeberg.org/Benjamin_Loison/astropy/issues/2).
fixedImagePart = fft1Part.copy()
height, width = fft1Part.shape
for x in range(width):
fft1Part[height // 2, x] = np.nan
#fft1Part[height // 2, x] = np.nan
fixedImagePart[height // 2, x] = np.mean([fft1Part[height // 2 - 1, x], fft1Part[height // 2 + 1, x]])
middleX = width // 2
RANGE = 1
for verticalLineX in [width // 4, width // 2, round(3 * width / 4)]:
for y in range(height):
for x in range(verticalLineX - RANGE, verticalLineX + RANGE + 1):
fft1Part[y, x] = np.nan
#fft1Part[y, x] = np.nan
fixedImagePart[y, x] = np.mean([fft1Part[y, x - RANGE - 1], fft1Part[y, x + RANGE + 1]])
# We smooth with a Gaussian kernel
kernel = Gaussian2DKernel(x_stddev = X_STDDEV)
#kernel = Gaussian2DKernel(x_stddev = X_STDDEV)
# create a "fixed" image with NaNs replaced by interpolated values
fixedImage = interpolate_replace_nans(fft1Part, kernel)
return fixedImage
# See [Benjamin_Loison/astropy/issues/1](https://codeberg.org/Benjamin_Loison/astropy/issues/1).
#fixedImagePart = interpolate_replace_nans(fft1Part, kernel)
return fixedImagePart
'''
realFixedImage = removePeriodicPatterns(np.real(fft1).copy())
imaginaryFixedImage = removePeriodicPatterns(np.imag(fft1).copy())
realFixedImage = removePeriodicPatterns(np.real(fft1))
imaginaryFixedImage = removePeriodicPatterns(np.imag(fft1))
fixedImage = realFixedImage + 1j * imaginaryFixedImage
'''
fixedImage = removePeriodicPatterns(abs(fft1))
figure, axes = plt.subplots(1, 2, sharex = True, sharey = True)
plt.suptitle('Attenuating FFT significant lines')
axes[0].set_title('Original FFT')
#originalFft1Abs = abs(originalFft1)
#minValue = np.min()
firstImage = np.log10(abs(originalFft1))
secondImage = np.log10(abs(fixedImage))
firstImage = np.log10(1 + abs(fft1))
secondImage = np.log10(1 + abs(fixedImage))
images = [firstImage, secondImage]
vMin = np.min(images)
vMax = np.max(images)
@ -71,20 +70,26 @@ axes[1].set_title('FFT with significant lines attenuated')
axes[1].imshow(secondImage, vmin = vMin, vmax = vMax)
plt.tight_layout()
plt.show()
#plt.imsave('fft.png', np.log10(fixedImage))
figure, axes = plt.subplots(1, 2, sharex = True, sharey = True)
figure, axes = plt.subplots(1, 3, sharex = True, sharey = True)
def inverseFft(fft):
ifft2 = np.real(fftpack.ifft2(fftpack.ifftshift(fft)))
#ifft2 = np.maximum(0, np.minimum(ifft2, 255)) #* 255# / 255
return ifft2
invFft1 = inverseFft(fft1)
invFixedImage = inverseFft(fixedImage)
images = [invFft1, invFixedImage]
vMin = np.min(images)
vMax = np.max(images)
plt.suptitle('Rafael 23/04/24 PRNU mean denoiser with periodic patterns attenuated')
axes[0].set_title('Original PRNU')
invOriginalFft1 = inverseFft(originalFft1)
axes[0].imshow(invOriginalFft1)
axes[0].imshow(invFft1, vmin = vMin, vmax = vMax)
axes[1].set_title('PRNU with periodic patterns attenuated')
axes[1].imshow(inverseFft(fixedImage), vmin = invOriginalFft1.min(), vmax = invOriginalFft1.max())
axes[1].imshow(invFixedImage, vmin = vMin, vmax = vMax)
axes[2].set_title('Difference between both left images')
differenceBetweenBothImages = invFft1 - invFixedImage
# `np.log10(1 + abs(differenceBetweenBothImages))` does not seem more interesting.
axes[2].imshow(differenceBetweenBothImages)
plt.tight_layout()
plt.show()
plt.show()