diff --git a/datasets/raise/fft/remove_period_patterns.py b/datasets/raise/fft/remove_period_patterns.py
index ed93d80..ad7a24c 100644
--- a/datasets/raise/fft/remove_period_patterns.py
+++ b/datasets/raise/fft/remove_period_patterns.py
@@ -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):
@@ -45,23 +45,20 @@ def removePeriodicPatterns(fft1Part):
     kernel = Gaussian2DKernel(x_stddev = X_STDDEV)
 
     # create a "fixed" image with NaNs replaced by interpolated values
-    fixedImage = interpolate_replace_nans(fft1Part, kernel)
-    return fixedImage
+    fixedImagePart = interpolate_replace_nans(fft1Part, kernel)
+    return fixedImagePart
 
-'''
+# TODO: are `.copy()` really necessary?
 realFixedImage = removePeriodicPatterns(np.real(fft1).copy())
 imaginaryFixedImage = removePeriodicPatterns(np.imag(fft1).copy())
 fixedImage = realFixedImage + 1j * imaginaryFixedImage
-'''
-fixedImage = removePeriodicPatterns(abs(fft1))
+fixedImage = imaginaryFixedImage
 
 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(1 + abs(originalFft1))
+firstImage = np.log10(1 + abs(np.real(originalFft1)))
 secondImage = np.log10(1 + abs(fixedImage))
 images = [firstImage, secondImage]
 vMin = np.min(images)
@@ -71,20 +68,26 @@ 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(1 + 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
 
+invOriginalFft1 = inverseFft(originalFft1)
+invFixedImage = inverseFft(fixedImage)
+images = [invOriginalFft1, 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(invOriginalFft1, 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 = invOriginalFft1 - invFixedImage
+# `np.log10(1 + abs(differenceBetweenBothImages))` does not seem more interesting.
+axes[2].imshow(differenceBetweenBothImages)
 plt.tight_layout()
 plt.show()
\ No newline at end of file