diff --git a/datasets/noise_free_test_images/estimate_prnu.py b/datasets/noise_free_test_images/estimate_prnu.py
index 932f1df..0886c20 100644
--- a/datasets/noise_free_test_images/estimate_prnu.py
+++ b/datasets/noise_free_test_images/estimate_prnu.py
@@ -34,7 +34,12 @@ 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):
+    imShow = axis.imshow(im)
+    plt.colorbar(imShow, label = 'Intensity', ax = axis, orientation = 'horizontal')
+
 for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
+    print(f'{splitNXN=}')
     IMAGE_SIZE_SHAPE = [dimension // splitNXN for dimension in (704, 469)]
 
     #prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
@@ -67,16 +72,16 @@ for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
                     axis = axes[0]
 
                     axis[0].set_title('First image without noise')
-                    axis[0].imshow(imageWithoutPrnuNpArrayTile)
+                    axisImShow(axis[0], imageWithoutPrnuNpArrayTile)
 
                     axis[1].set_title('Actual Gaussian noised PRNU')
-                    axis[1].imshow(prnuNpArray)
+                    axisImShow(axis[1], prnuNpArray)
 
                     axis[2].set_title('F. i. with G. n.')
-                    axis[2].imshow(imageWithoutPrnuNpArray + imageNoise)
+                    axisImShow(axis[2], imageWithoutPrnuNpArray + imageNoise)
 
                     axis[3].set_title('F. i. with G. n. and PRNU')
-                    axis[3].imshow(imageWithoutPrnuNpArray + prnuNpArray + imageNoise)
+                    axisImShow(axis[3], imageWithoutPrnuNpArray + prnuNpArray + imageNoise)
                 isFirstImage = False
 
                 #assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image'