diff --git a/datasets/raise/fft/remove_period_patterns.py b/datasets/raise/fft/remove_period_patterns.py
index 6b86884..52114ce 100644
--- a/datasets/raise/fft/remove_period_patterns.py
+++ b/datasets/raise/fft/remove_period_patterns.py
@@ -22,8 +22,9 @@ singleColorChannelImages = {color: getImageByColor(color) for color in Color}
 multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
 image = multipleColorsImage
 
-fft1 = abs(fftpack.fft2(image))
+fft1 = fftpack.fftshift(fftpack.fft2(image))
 originalFft1 = fft1.copy()
+fft1 = abs(fft1)
 
 # This example is intended to demonstrate how astropy.convolve and
 # scipy.convolve handle missing data, so we start by setting the brightest
@@ -47,9 +48,26 @@ fixedImage = interpolate_replace_nans(fft1, kernel)
 
 figure, axes = plt.subplots(1, 2, sharex = True, sharey = True)
 
+plt.suptitle('Attenuating FFT significant lines')
 axes[0].set_title('Original FFT')
-axes[0].imshow(np.log10(originalFft1))
+axes[0].imshow(np.log10(abs(originalFft1)))
 axes[1].set_title('FFT with significant lines attenuated')
 axes[1].imshow(np.log10(fixedImage))
+plt.tight_layout()
 plt.show()
-#plt.imsave('fft.png', np.log10(fixedImage))
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+#plt.imsave('fft.png', np.log10(fixedImage))
+
+figure, axes = plt.subplots(1, 2, 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
+
+plt.suptitle('Rafael 23/04/24 PRNU mean denoiser with periodic patterns attenuated')
+axes[0].set_title('Original PRNU')
+axes[0].imshow(inverseFft(originalFft1))
+axes[1].set_title('PRNU with periodic patterns attenuated')
+axes[0].imshow(inverseFft(fixedImage))
+plt.tight_layout()
+plt.show()
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