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
new file mode 100755
index 0000000..7095b17
--- /dev/null
+++ b/datasets/raise/merge_with_same_color_scale_single_color_channel_images_according_to_bayer_filter.py
@@ -0,0 +1,36 @@
+#!/usr/bin/env python
+
+from utils import Denoiser, Color, mergeSingleColorChannelImagesAccordingToBayerFilter
+import matplotlib.pyplot as plt
+import numpy as np
+
+PREFIX = 'means/mean_rafael_230424_'
+
+def getImageByColor(color):
+    filePath = PREFIX + f'{color}.npy'
+    image = np.load(filePath)
+    return image
+
+multipleColorsImageDenoisers = {}
+
+DENOISERS = [
+    Denoiser.BILATERAL,
+    Denoiser.WAVELET,
+    Denoiser.MEAN,
+]
+
+for denoiser in DENOISERS:
+    singleColorChannelImages = {color: getImageByColor(color) for color in Color}
+    multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
+    multipleColorsImageDenoisers[denoiser] = multipleColorsImage
+
+def getExtreme(extreme):
+    vExtreme = extreme(list(multipleColorsImageDenoisers.values()))
+    #print(vExtreme)
+    return vExtreme
+
+vMin, vMax = [getExtreme(extreme) for extreme in [np.min, np.max]]
+
+for denoiser in DENOISERS:
+    multipleColorsImage = multipleColorsImageDenoisers[denoiser]
+    plt.imsave(PREFIX + f'{denoiser}_multiple_colors.png', multipleColorsImage)