diff --git a/datasets/raise/merge_with_same_color_scale_single_color_channel_images_according_to_bayer_filter.py b/datasets/raise/merge_with_same_color_scale_single_color_channel_images_according_to_bayer_filter.py
index b50383a..37d41f5 100755
--- a/datasets/raise/merge_with_same_color_scale_single_color_channel_images_according_to_bayer_filter.py
+++ b/datasets/raise/merge_with_same_color_scale_single_color_channel_images_according_to_bayer_filter.py
@@ -3,11 +3,19 @@
 from utils import Denoiser, Color, mergeSingleColorChannelImagesAccordingToBayerFilter
 import matplotlib.pyplot as plt
 import numpy as np
+import os
 
-PREFIX = 'means/mean_rafael_230424_'
+PREFIX = 'means/mean_flat-field_nef_'
+
+fileNames = os.listdir(os.path.dirname(PREFIX))
+fileNamesLowercase = list(map(str.lower, fileNames))
+
+# Assume that there are no 2 file names just differing by case.
+def getCaseInsensitiveFilePath(filePath):
+    return os.path.dirname(filePath) + '/' + fileNames[fileNamesLowercase.index(os.path.basename(filePath).lower())]
 
 def getImageByColor(color, denoiser):
-    filePath = PREFIX + f'{denoiser}_{color}.npy'
+    filePath = getCaseInsensitiveFilePath(PREFIX + f'{denoiser}_{color}.npy')
     image = np.load(filePath)
     return image
 
@@ -19,13 +27,25 @@ DENOISERS = [
     Denoiser.MEAN,
 ]
 
-for denoiser in DENOISERS:
+figure, axes = plt.subplots(nrows = len(DENOISERS) + 1)
+plt.suptitle('PRNU estimate distribution of values for RAISE flat-field images')
+
+for denoiserIndex, denoiser in enumerate(DENOISERS):
     singleColorChannelImages = {color: getImageByColor(color, denoiser) for color in Color}
     multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
     multipleColorsImageDenoisers[denoiser] = multipleColorsImage
+    axis = axes[denoiserIndex]
+    labels = [str(color).replace('_', ' ') for color in Color]
+    axis.boxplot(list(map(np.ravel, singleColorChannelImages.values())), labels = labels)
+    axis.grid(True)
+    axis.set_title(denoiser)
 
-plt.boxplot(list(map(np.ravel, multipleColorsImageDenoisers.values())), labels = DENOISERS)
-plt.grid(True)
+axis = axes[-1]
+axis.boxplot(list(map(np.ravel, multipleColorsImageDenoisers.values())), labels = DENOISERS)
+axis.grid(True)
+axis.set_title('Merged single color channels')
+
+plt.tight_layout()
 plt.show()
 
 def getExtreme(extreme):