Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Clean low pass denoiser

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Benjamin Loison 2024-05-15 18:21:11 +02:00
parent 95796b53fc
commit ec4657eda8
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1 changed files with 9 additions and 15 deletions
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datasets/raise/utils.py
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@ -60,28 +60,22 @@ def denoise(imageNpArray, denoiser):
# fft of image
fft1 = fftpack.fftshift(fftpack.fft2(imageNpArray))
# Padding as follows may not be perfectly correct but seems fine enough.
GAUSSIAN_SIGMA = 50
#low_pass_np = np.zeros(image1_np.shape, dtype = np.uint8)
low_pass_np_size = min(image1_np.shape)
low_pass_np = getGaussianKernel(low_pass_np_size, GAUSSIAN_SIGMA)
imageNpArrayShape = imageNpArray.shape
lowPassNpArraySize = min(imageNpArrayShape)
lowPassNpArray = getGaussianKernel(lowPassNpArraySize, GAUSSIAN_SIGMA)
#rectangle_low_pass_np = np.zeros(fft1.shape)
#rectangle_low_pass_np[]
requiredH, requiredW = image1_np.shape
vert_pad_size = (requiredH - low_pass_np_size) // 2
hor_pad_size = (requiredW - low_pass_np_size) // 2
rectangle_low_pass_np = np.pad(low_pass_np, [(vert_pad_size, vert_pad_size), (hor_pad_size, hor_pad_size)])
wantedHeight, wantedWidth = imageNpArrayShape
verticalPadSize, horizontalPadSize = [(wantedDimension - lowPassNpArraySize) // 2 for wantedDimension in [wantedHeight, wantedWidth]]
rectangleLowPassNpArray = np.pad(lowPassNpArray, [(verticalPadSize, verticalPadSize), (horizontalPadSize, horizontalPadSize)])
# Unsure that doing such *normalization* is appropriate.
rectangle_low_pass_np /= rectangle_low_pass_np.max()
#plt.imshow(rectangle_low_pass_np)
#plt.show()
rectangleLowPassNpArray /= rectangleLowPassNpArray.max()
# multiply both the images
filtered = np.multiply(fft1, lowPassNp)
filtered = np.multiply(fft1, rectangleLowPassNpArray)
# inverse fft
ifft2 = np.real(fftpack.ifft2(fftpack.ifftshift(filtered)))