diff --git a/digital_image_processing/filters/gabor_filter.py b/digital_image_processing/filters/gabor_filter.py
new file mode 100644
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+++ b/digital_image_processing/filters/gabor_filter.py
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+# Implementation of the Gaborfilter
+# https://en.wikipedia.org/wiki/Gabor_filter
+import numpy as np
+from cv2 import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filter2D, imread, imshow, waitKey
+
+
+def gabor_filter_kernel(
+    ksize: int, sigma: int, theta: int, lambd: int, gamma: int, psi: int
+) -> np.ndarray:
+    """
+    :param ksize:   The kernelsize of the convolutional filter (ksize x ksize)
+    :param sigma:   standard deviation of the gaussian bell curve
+    :param theta:   The orientation of the normal to the parallel stripes
+                    of Gabor function.
+    :param lambd:   Wavelength of the sinusoidal component.
+    :param gamma:   The spatial aspect ratio and specifies the ellipticity
+                    of the support of Gabor function.
+    :param psi:     The phase offset of the sinusoidal function.
+
+    >>> gabor_filter_kernel(3, 8, 0, 10, 0, 0).tolist()
+    [[0.8027212023735046, 1.0, 0.8027212023735046], [0.8027212023735046, 1.0, \
+0.8027212023735046], [0.8027212023735046, 1.0, 0.8027212023735046]]
+
+    """
+
+    # prepare kernel
+    # the kernel size have to be odd
+    if (ksize % 2) == 0:
+        ksize = ksize + 1
+    gabor = np.zeros((ksize, ksize), dtype=np.float32)
+
+    # each value
+    for y in range(ksize):
+        for x in range(ksize):
+            # distance from center
+            px = x - ksize // 2
+            py = y - ksize // 2
+
+            # degree to radiant
+            _theta = theta / 180 * np.pi
+            cos_theta = np.cos(_theta)
+            sin_theta = np.sin(_theta)
+
+            # get kernel x
+            _x = cos_theta * px + sin_theta * py
+
+            # get kernel y
+            _y = -sin_theta * px + cos_theta * py
+
+            # fill kernel
+            gabor[y, x] = np.exp(
+                -(_x ** 2 + gamma ** 2 * _y ** 2) / (2 * sigma ** 2)
+            ) * np.cos(2 * np.pi * _x / lambd + psi)
+
+    return gabor
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()
+    # read original image
+    img = imread("../image_data/lena.jpg")
+    # turn image in gray scale value
+    gray = cvtColor(img, COLOR_BGR2GRAY)
+
+    # Apply multiple Kernel to detect edges
+    out = np.zeros(gray.shape[:2])
+    for theta in [0, 30, 60, 90, 120, 150]:
+        """
+        ksize = 10
+        sigma = 8
+        lambd = 10
+        gamma = 0
+        psi = 0
+        """
+        kernel_10 = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
+        out += filter2D(gray, CV_8UC3, kernel_10)
+    out = out / out.max() * 255
+    out = out.astype(np.uint8)
+
+    imshow("Original", gray)
+    imshow("Gabor filter with 20x20 mask and 6 directions", out)
+
+    waitKey(0)