diff --git a/datasets/noise_free_test_images/estimate_prnu.py b/datasets/noise_free_test_images/estimate_prnu.py
index 820e44e..e3bb72e 100644
--- a/datasets/noise_free_test_images/estimate_prnu.py
+++ b/datasets/noise_free_test_images/estimate_prnu.py
@@ -34,9 +34,8 @@ SPLIT_N_X_N_S = [1, 2, 4]
 fig, axes = plt.subplots(2, 4)
 fig.suptitle('PRNU estimation with different number of images having Gaussian noise and Gaussian noised PRNU')
 
-def axisImShow(axis, im, cMap = (None, None)):
-    vMin, vMax = cMap
-    imShow = axis.imshow(im, vmin = vMin, vmax = vMax)
+def axisImShow(axis, im):
+    imShow = axis.imshow(im)
     plt.colorbar(imShow, label = 'Intensity', ax = axis, orientation = 'horizontal')
 
 for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
@@ -72,26 +71,20 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
                 if splitNXNIndex == 0 and isFirstImage:
                     axis = axes[0]
 
-                    images = [imageWithoutPrnuNpArrayTile, imageWithoutPrnuNpArray + imageNoise, imageWithoutPrnuNpArray + prnuNpArray + imageNoise]
-                    vMin = np.min(images)#min([np.min(image) for image in [imageWithoutPrnuNpArrayTile, imageWithoutPrnuNpArray + imageNoise, imageWithoutPrnuNpArray + prnuNpArray + imageNoise]])
-                    vMax = np.max(images)
-                    cMap = (vMin, vMax)
-                    #print(f'{vMin=}')
-
                     axis[0].set_title('First image without noise')
-                    axisImShow(axis[0], imageWithoutPrnuNpArrayTile, cMap = cMap)
+                    axisImShow(axis[0], imageWithoutPrnuNpArrayTile)
 
                     axis[1].set_title('First image Gaussian noise')
                     axisImShow(axis[1], imageNoise)
 
                     axis[2].set_title('First image with Gaussian noise')
-                    axisImShow(axis[2], imageWithoutPrnuNpArray + imageNoise, cMap = cMap)
+                    axisImShow(axis[2], np.clip(imageWithoutPrnuNpArray + imageNoise, 0, 255))
 
                     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], imageWithoutPrnuNpArray + prnuNpArray + imageNoise, cMap = cMap)
+                    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'