diff --git a/algorithms/image_utils/image_utils.py b/algorithms/image_utils/image_utils.py
index 16afe67..a152207 100644
--- a/algorithms/image_utils/image_utils.py
+++ b/algorithms/image_utils/image_utils.py
@@ -31,4 +31,11 @@ def getPrnuShownAsSuch(size, gaussianNoise = 0):
     # Center vertically, especially in the case `HEIGHT` > `WIDTH`.
     draw.text((0, HEIGHT // 2 - fontSize // 2), TEXT, 255, font = font)
     imageNpArray = np.array(imagePil)
-    return imageNpArray + randomGaussianImage(gaussianNoise, size[::-1])
\ No newline at end of file
+    gaussianNoiseNpArray = randomGaussianImage(gaussianNoise, size[::-1])
+    #prnuShownAsSuch = imageNpArray + gaussianNoiseNpArray
+    prnuShownAsSuch = imageNpArray
+    for y in range(HEIGHT):
+        for x in range(WIDTH):
+            if prnuShownAsSuch[y, x] != 0:
+                prnuShownAsSuch[y, x] += gaussianNoiseNpArray[y, x]
+    return prnuShownAsSuch
\ No newline at end of file
diff --git a/datasets/noise_free_test_images/estimate_prnu.py b/datasets/noise_free_test_images/estimate_prnu.py
index 5603c63..91e492c 100644
--- a/datasets/noise_free_test_images/estimate_prnu.py
+++ b/datasets/noise_free_test_images/estimate_prnu.py
@@ -25,17 +25,14 @@ datasetPath = 'no_noise_images'
 # 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 = 0.01
 NOISE_FACTOR = 0.1
-SPLIT_N_X_N = 4
+SPLIT_N_X_N = 1
 
 IMAGE_SIZE_SHAPE = [dimension // SPLIT_N_X_N for dimension in (704, 469)]
 
 np.random.seed(0)
 
 #prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
-prnuNpArray = getPrnuShownAsSuch(IMAGE_SIZE_SHAPE, 100) * PRNU_FACTOR
-showImageWithMatplotlib(prnuNpArray)
-
-##
+prnuNpArray = getPrnuShownAsSuch(IMAGE_SIZE_SHAPE, 255) * PRNU_FACTOR
 
 def isIn256Range(x):
     return 0 <= x and x <= 255