diff --git a/datasets/raise/attribute_source_camera.py b/datasets/raise/attribute_source_camera.py
index 7643c04..05b583f 100755
--- a/datasets/raise/attribute_source_camera.py
+++ b/datasets/raise/attribute_source_camera.py
@@ -3,7 +3,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from tqdm import tqdm
-from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray
+from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray, getColorMeans
 import sys
 import os
 import random
@@ -12,7 +12,7 @@ sys.path.insert(0, '../../algorithms/distance/')
 
 from rms_diff import rmsDiffNumpy
 
-DENOISER = 'wavelet'
+DENOISER = 'mean'
 IMAGES_CAMERAS_FOLDER = {
     'RAISE': 'flat-field/nef',
     'Rafael 23/04/24': 'rafael/230424',
@@ -27,7 +27,7 @@ random.seed(0)
 for camera in IMAGES_CAMERAS_FOLDER:
     random.shuffle(imagesCamerasFileNames[camera])
 
-minimumNumberOfImagesCameras = 16#min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
+minimumNumberOfImagesCameras = min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
 for camera in IMAGES_CAMERAS_FOLDER:
     imagesCamerasFileNames[camera] = imagesCamerasFileNames[camera][:minimumNumberOfImagesCameras]
     print(camera, imagesCamerasFileNames[camera])
@@ -67,12 +67,16 @@ def getMultipleColorsImage(singleColorChannelImages):
     multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
     return multipleColorsImage
 
-def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage):
-    singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
+def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera):
+    singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) if DENOISER != 'mean' else cameraColorMeans[camera][color] for color in Color}
     multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
     imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
     return imagePrnuEstimateNpArray
 
+imagesCamerasFilePaths = {camera: [f'{IMAGES_CAMERAS_FOLDER[camera]}/{imagesCamerasFileName}' for imagesCamerasFileName in imagesCamerasFileNames[camera]] for camera in imagesCamerasFileNames}
+#print(imagesCamerasFilePaths)
+cameraColorMeans = {camera: getColorMeans(imagesCamerasFilePaths[camera], Color, DENOISER) for camera in imagesCamerasFilePaths}
+
 from utils import silentTqdm
 #tqdm = silentTqdm
 
@@ -90,7 +94,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                 singleColorChannelImages = getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
                 multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
 
-                imagePrnuEstimateNpArray = getImagePrnuEstimatedNpArray(singleColorChannelImages, multipleColorsImage)
+                imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
 
                 cameraTestingImagesNoise[camera] = cameraTestingImagesNoise.get(camera, []) + [imagePrnuEstimateNpArray]
     for cameraTrainingImageIndex in tqdm(range(minimumNumberOfImagesCameras if computeExtremes else numberOfTrainingImages), 'Camera training image index'):
@@ -102,7 +106,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                 minColor, maxColor = updateExtremes(multipleColorsImage, minColor, maxColor)
                 continue
 
-            imagePrnuEstimateNpArray = getImagePrnuEstimatedNpArray(singleColorChannelImages, multipleColorsImage)
+            imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
 
             cameraIterativeMean = camerasIterativeMean[camera]
             cameraIterativeMean.add(imagePrnuEstimateNpArray)