diff --git a/datasets/raise/extract_noise.py b/datasets/raise/extract_noise.py
index 9ff8d12..0e0ca46 100755
--- a/datasets/raise/extract_noise.py
+++ b/datasets/raise/extract_noise.py
@@ -16,13 +16,15 @@ imagesFolderPathFileName = imagesFolderPath.replace('/', '_')
 # - `wavelet`
 # - `bilateral`
 # - `tv_chambolle`
-denoiser = 'bilateral'
+# - `mean`
+denoiser = 'mean'
 
 raiseNotFlatFields = False
 # `[Color.RED, Color.GREEN_RIGHT, ...]` or `Color`.
 colors = Color
 
-denoise = getattr(skimage.restoration, f'denoise_{denoiser}')
+if denoiser != 'mean':
+    denoise = getattr(skimage.restoration, f'denoise_{denoiser}')
 
 imagesFileNames = os.listdir(imagesFolderPath + ('/png' if raiseNotFlatFields else ''))
 
@@ -114,6 +116,8 @@ def treatImage(imageFileName, computeExtremes = False, color = None):
             imageDenoisedNpArray = denoise(imageNpArray, sigma_color=0.05, sigma_spatial=15)
         case 'tv_chambolle':
             imageDenoisedNpArray = denoise(imageNpArray, weight=0.2)
+        case 'mean':
+            imageDenoisedNpArray = imageNpArray - means[color]
     imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
     if mean is None:
         mean = imageNoiseNpArray
@@ -131,6 +135,21 @@ for imageFileName in tqdm(imagesFileNames, 'Computing extremes of images'):
 print(f'{minColor=}')
 print(f'{maxColor=}')
 
+if denoiser == 'mean':
+    means = {}
+    for color in Color:
+        colorMean = None
+        numberOfImagesInColorMean = 0
+        for imageFileName in tqdm(imagesFileNames, f'Computing mean of {color} images'):
+            #print(imageFileName)
+            imageNpArray = getImageNpArray(imageFileName, False, color)
+            if colorMean is None:
+                colorMean = imageNpArray
+            else:
+                colorMean = ((colorMean * numberOfImagesInColorMean) + imageNpArray) / (numberOfImagesInColorMean + 1)
+            numberOfImagesInColorMean += 1
+        means[color] = colorMean
+
 for color in colors:
     mean = None
     numberOfImagesInMean = 0