diff --git a/digital_image_processing/__init__.py b/digital_image_processing/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/digital_image_processing/filters/__init__.py b/digital_image_processing/filters/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/digital_image_processing/filters/median_filter.py b/digital_image_processing/filters/median_filter.py
new file mode 100644
index 000000000..eea429563
--- /dev/null
+++ b/digital_image_processing/filters/median_filter.py
@@ -0,0 +1,42 @@
+"""
+Implementation of median filter algorithm
+"""
+
+from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
+from numpy import zeros_like, ravel, sort, multiply, divide, int8
+
+
+def median_filter(gray_img, mask=3):
+    """
+    :param gray_img: gray image
+    :param mask: mask size
+    :return: image with median filter
+    """
+    # set image borders
+    bd = int(mask / 2)
+    # copy image size
+    median_img = zeros_like(gray)
+    for i in range(bd, gray_img.shape[0] - bd):
+        for j in range(bd, gray_img.shape[1] - bd):
+            # get mask according with mask
+            kernel = ravel(gray_img[i - bd:i + bd + 1, j - bd:j + bd + 1])
+            # calculate mask median
+            median = sort(kernel)[int8(divide((multiply(mask, mask)), 2) + 1)]
+            median_img[i, j] = median
+    return median_img
+
+
+if __name__ == '__main__':
+    # read original image
+    img = imread('lena.jpg')
+    # turn image in gray scale value
+    gray = cvtColor(img, COLOR_BGR2GRAY)
+
+    # get values with two different mask size
+    median3x3 = median_filter(gray, 3)
+    median5x5 = median_filter(gray, 5)
+
+    # show result images
+    imshow('median filter with 3x3 mask', median3x3)
+    imshow('median filter with 5x5 mask', median5x5)
+    waitKey(0)