diff --git a/datasets/noise_free_test_images/estimate_prnu.py b/datasets/noise_free_test_images/estimate_prnu.py
index 41e3fb2..5654686 100644
--- a/datasets/noise_free_test_images/estimate_prnu.py
+++ b/datasets/noise_free_test_images/estimate_prnu.py
@@ -19,7 +19,7 @@ from skimage.restoration import denoise_tv_chambolle
 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_FACTORS = [0.1, 0.01]
+PRNU_FACTOR = 0.1
 IMAGE_SIZE_SHAPE = (469, 704)
 
 np.random.seed(0)
@@ -39,31 +39,11 @@ for imageName in os.listdir(datasetPath):
         imageWithoutPrnuPil = Image.open(imagePath).convert('F')
         imageWithoutPrnuNpArray = np.array(imageWithoutPrnuPil)
 
-        fig, axes = plt.subplots(3, 2)
-        fig.suptitle('Single PRNU estimation from an image with PRNU')
-
-        axes[0][0].set_title('Actual PRNU')
-        axes[0][0].imshow(prnuNpArray)
-
-        axes[0][1].axis('off')
-
-        for prnuIndex, prnuNpArray in enumerate(prnusNpArray):
-            imageWithPrnuNpArray = imageWithoutPrnuNpArray + prnuNpArray
-            #assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image'
-            imageWithPrnuPil = toPilImage(imageWithPrnuNpArray)
-            #imagePrnuEstimatePil = contextAdaptiveInterpolator(imageWithPrnuPil.load(), imageWithPrnuPil)
-            #imagePrnuEstimateNpArray = np.array(imagePrnuEstimatePil)
-            imagePrnuEstimateNpArray = imageWithPrnuNpArray - denoise_tv_chambolle(imageWithPrnuNpArray, weight=0.2, channel_axis=-1)
-            axis = axes[prnuIndex + 1]
-
-            axis[0].set_title(f'Image with PRNU\nPRNU_FACTOR = {PRNU_FACTORS[prnuIndex]}')
-            axis[0].imshow(imageWithPrnuNpArray)
-
-            axis[1].set_title('PRNU estimate')
-            axis[1].imshow(imagePrnuEstimateNpArray)
-        break
+        imageWithPrnuNpArray = imageWithoutPrnuNpArray + prnuNpArray
+        #assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image'
+        imageWithPrnuPil = toPilImage(imageWithPrnuNpArray)
+        #imagePrnuEstimatePil = contextAdaptiveInterpolator(imageWithPrnuPil.load(), imageWithPrnuPil)
+        #imagePrnuEstimateNpArray = np.array(imagePrnuEstimatePil)
+        imagePrnuEstimateNpArray = imageWithPrnuNpArray - denoise_tv_chambolle(imageWithPrnuNpArray, weight=0.2, channel_axis=-1)
 
         imagesPrnuEstimateNpArray += [imagePrnuEstimateNpArray]
-
-plt.tight_layout(pad = 0)
-plt.show()