diff --git a/datasets/noise_free_test_images/estimate_prnu.py b/datasets/noise_free_test_images/estimate_prnu.py
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+++ b/datasets/noise_free_test_images/estimate_prnu.py
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+# Notes: https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/25
+
+import os
+from PIL import Image
+import numpy as np
+import sys
+
+sys.path.insert(0, '../../algorithms/image_utils/')
+
+from image_utils import showImageWithMatplotlib, randomGaussianImage
+
+datasetPath = 'no_noise_images'
+# Note that contrarily to `datasets/fake/`, here we do not have images being Gaussian with `scale` `1` but actual images with pixel values between 0 and 255.
+# In addition to the range difference, note that the distribution in the first set of images was a Gaussian and here is very different and specific.
+PRNU_FACTOR = 255 * 0.1
+IMAGE_SIZE_SHAPE = (469, 704)
+
+# Should first try with an image showing `PRNU`.
+prnu = randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
+
+for imageName in os.listdir(datasetPath):
+    if imageName.endswith('.png'):
+        #print(imageName)
+        imagePath = f'{datasetPath}/{imageName}'
+        imagePil = Image.open(imagePath).convert('F')
+        imageNpArray = np.array(imagePil)
+        showImageWithMatplotlib(imageNpArray)
+        showImageWithMatplotlib(imageNpArray + prnu)
+        break
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