diff --git a/algorithms/image_utils/image_utils.py b/algorithms/image_utils/image_utils.py
index a152207..677f23b 100644
--- a/algorithms/image_utils/image_utils.py
+++ b/algorithms/image_utils/image_utils.py
@@ -30,12 +30,15 @@ def getPrnuShownAsSuch(size, gaussianNoise = 0):
             break
     # Center vertically, especially in the case `HEIGHT` > `WIDTH`.
     draw.text((0, HEIGHT // 2 - fontSize // 2), TEXT, 255, font = font)
-    imageNpArray = np.array(imagePil)
+    imageNpArray = np.array(imagePil, dtype = np.float64)
+    #print(imageNpArray.dtype)
+    #exit(1)
     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]
+                #print(prnuShownAsSuch[y, x])
+                prnuShownAsSuch[y, x] = gaussianNoiseNpArray[y, x]
     return prnuShownAsSuch
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diff --git a/datasets/noise_free_test_images/estimate_prnu.py b/datasets/noise_free_test_images/estimate_prnu.py
index 0886c20..e3bb72e 100644
--- a/datasets/noise_free_test_images/estimate_prnu.py
+++ b/datasets/noise_free_test_images/estimate_prnu.py
@@ -74,14 +74,17 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
                     axis[0].set_title('First image without noise')
                     axisImShow(axis[0], imageWithoutPrnuNpArrayTile)
 
-                    axis[1].set_title('Actual Gaussian noised PRNU')
-                    axisImShow(axis[1], prnuNpArray)
+                    axis[1].set_title('First image Gaussian noise')
+                    axisImShow(axis[1], imageNoise)
 
-                    axis[2].set_title('F. i. with G. n.')
-                    axisImShow(axis[2], imageWithoutPrnuNpArray + imageNoise)
+                    axis[2].set_title('First image with Gaussian noise')
+                    axisImShow(axis[2], np.clip(imageWithoutPrnuNpArray + imageNoise, 0, 255))
 
-                    axis[3].set_title('F. i. with G. n. and PRNU')
-                    axisImShow(axis[3], imageWithoutPrnuNpArray + prnuNpArray + imageNoise)
+                    axis[3].set_title('Actual Gaussian noised PRNU')
+                    axisImShow(axis[3], prnuNpArray)
+
+                    axes[1][0].set_title('First image with Gaussian noise and PRNU')
+                    axisImShow(axes[1][0], np.clip(imageWithoutPrnuNpArray + prnuNpArray + imageNoise, 0, 255))
                 isFirstImage = False
 
                 #assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image'
@@ -96,10 +99,8 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
     rms = rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True)
     title = f'RMS with actual PRNU: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray):.4f}\n(normalized RMS: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True):.4f})'
     axis = axes[1]
-    axis[splitNXNIndex].set_title(f'Number of images: {len(imagesPrnuEstimateNpArray)}\n{title}')
-    axis[splitNXNIndex].imshow(cameraPrnuEstimateNpArray)
-
-axes[1][3].axis('off')
+    axis[splitNXNIndex + 1].set_title(f'PRNU estimation\nwith {len(imagesPrnuEstimateNpArray)} images')#\n{title}
+    axisImShow(axis[splitNXNIndex + 1], cameraPrnuEstimateNpArray)
 
 plt.tight_layout()
 plt.show()
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