diff --git a/datasets/raise/attribute_source_camera.py b/datasets/raise/attribute_source_camera.py
index 6ba705e..9690f62 100755
--- a/datasets/raise/attribute_source_camera.py
+++ b/datasets/raise/attribute_source_camera.py
@@ -74,7 +74,6 @@ def getMultipleColorsImage(singleColorChannelImages):
     return multipleColorsImage
 
 def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera):
-    #singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) if DENOISER != Denoiser.MEAN else (cameraColorMeans[camera][color] if PREDICT_ONLY_ON_WHOLE_TRAINING_SET else (cameraColorMeans[camera][color].mean if )) for color in Color}
     singleColorChannelDenoisedImages = {}
     for color in Color:
         if DENOISER != Denoiser.MEAN:
@@ -85,15 +84,6 @@ def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, c
                 singleColorChannelDenoisedImage = cameraColorMean
             else:
                 cameraColorCurrentMean = cameraColorMean.mean
-                if cameraColorCurrentMean is None:
-                    print('`cameraColorCurrentMean` is `None`!')
-                    exit(2)
-                '''
-                if cameraColorCurrentMean is None:
-                    singleColorChannelDenoisedImage = singleColorChannelImages[color]
-                else:
-                    singleColorChannelDenoisedImage = cameraColorCurrentMean
-                '''
                 singleColorChannelDenoisedImage = cameraColorCurrentMean
         singleColorChannelDenoisedImages[color] = singleColorChannelDenoisedImage
     multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
@@ -149,10 +139,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
             imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
 
             cameraIterativeMean = camerasIterativeMean[camera]
-            #print(f'{imagePrnuEstimateNpArray=}')
-            #exit(2)
             cameraIterativeMean.add(imagePrnuEstimateNpArray)
-            #continue
             # If we are considering the last camera and (not `PREDICT_ONLY_ON_WHOLE_TRAINING_SET` or we are considering the last training image), then we proceeded an additional image for all cameras and we can predict the accuracy at this learning step.
             if cameraIndex == numberOfCameras - 1 and (not PREDICT_ONLY_ON_WHOLE_TRAINING_SET or cameraTrainingImageIndex == numberOfTrainingImages - 1):
                 numberOfTrainingImagesAccuracy = 0
@@ -172,7 +159,6 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                                 multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
                                 cameraTestingImageNoise = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
 
-                            #print(f'{cameraTestingImageNoise = }')
                             distance = rmsDiffNumpy(cameraTestingImageNoise, camerasIterativeMean[camera].mean)
                             print(f'{cameraTrainingImageIndex=} {cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
                             if minimalDistance is None or distance < minimalDistance: