diff --git a/datasets/raise/extract_4_color_channel_images_from_raw.py b/datasets/raise/extract_4_color_channel_images_from_raw.py
index 0c35b5f..30a48d1 100644
--- a/datasets/raise/extract_4_color_channel_images_from_raw.py
+++ b/datasets/raise/extract_4_color_channel_images_from_raw.py
@@ -1,8 +1,9 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from utils import Color, getColorChannel
+from tqdm import tqdm
 
-IMAGE_INDEX = 3294
+IMAGE_INDEX_RANGE = range(3294, 3553)
 Y_RANGE = 5
 X_RANGE = 25
 ORIGINAL_INTERESTING_POSITION = [5752, 1695][::-1]
@@ -16,16 +17,18 @@ def getImageColorChannel(imageIndex, color):
     imageFilePath = getImageFilePath(imageIndex)
     return getColorChannel(imageFilePath, color).astype(np.int32)
 
-firstColorChannel = getImageColorChannel(IMAGE_INDEX, COLOR)
-secondColorChannel = getImageColorChannel(IMAGE_INDEX + 1, COLOR)
-differenceColorChannel = firstColorChannel - secondColorChannel
-print(differenceColorChannel.shape)
+# Could leverage crop already here.
+colorChannels = [getImageColorChannel(imageIndex, COLOR) for imageIndex in tqdm(IMAGE_INDEX_RANGE)]
+meanColorChannels = np.mean(colorChannels, axis = 0)
+estimatedPrnus = [colorChannel - meanColorChannels for colorChannel in tqdm(colorChannels)]
+estimatedPrnu = np.mean(estimatedPrnus, axis = 0)
+print(estimatedPrnu.shape)
 
 def crop(image, interestingPosition, yRange, xRange):
     return image[interestingPosition[0] - yRange: interestingPosition[0] + yRange, interestingPosition[1] - xRange: interestingPosition[1] + xRange]
 
 #colorChannel = crop(multipleColorsImage, ORIGINAL_INTERESTING_POSITION, Y_RANGE, X_RANGE)
-colorChannelToDisplay = differenceColorChannel
+colorChannelToDisplay = estimatedPrnu
 colorChannelToDisplay = crop(colorChannelToDisplay, INTERESTING_POSITION, Y_RANGE, X_RANGE)
 print(colorChannelToDisplay.shape)
 plt.imshow(colorChannelToDisplay)