diff --git a/datasets/fake/generate_dataset.py b/datasets/fake/generate_dataset.py
index a6d419f..cb09315 100644
--- a/datasets/fake/generate_dataset.py
+++ b/datasets/fake/generate_dataset.py
@@ -13,16 +13,17 @@ from context_adaptive_interpolator import contextAdaptiveInterpolator
 
 IMAGE_SIZE = 64
 
+# Is such `np.maximum` probabilistically correct with our theoretical method?
 def randomImage(scale):
     return np.maximum(np.random.normal(loc = 0, scale = scale, size = (IMAGE_SIZE, IMAGE_SIZE)), 0)
 
 prnu = randomImage(scale = 1)
 
-images = [randomImage(scale = 10) + prnu for _ in range(10)]
+images = [randomImage(scale = 10) + prnu for _ in range(100)]
 allImages = [prnu] + images
 
 def toPilImage(npArray):
-    npArray = np.round(255 * npArray / np.max(allImages))
+    npArray = (255 * npArray / np.max(allImages)).round()
     return Image.fromarray(np.uint8(npArray))
 
 prnu = toPilImage(prnu)
@@ -31,7 +32,13 @@ prnu = toPilImage(prnu)
 images = [toPilImage(image) for image in images]
 #images[0].show()
 
-for image in images:
+caiImages = [contextAdaptiveInterpolator(image.load(), image) for image in images]
+for image, caiImage in zip(images, caiImages):
     initialRmsDiff = rmsdiff(image, prnu)
-    caiRmsDiff = rmsdiff(contextAdaptiveInterpolator(image.load(), image), prnu)
-    print(f'{initialRmsDiff=} {caiRmsDiff=}')
\ No newline at end of file
+    caiRmsDiff = rmsdiff(caiImage, prnu)
+    #print(f'{initialRmsDiff=} {caiRmsDiff=}')
+
+print('RMS diff with average image =', rmsdiff(toPilImage(np.array(images).mean(axis = 0)), prnu))
+# Should be better than above one. Maybe not the case here as the images are Gaussian noise.
+print('RMS diff with average CAI images =', rmsdiff(toPilImage(np.array(caiImages).mean(axis = 0)), prnu))
+