diff --git a/digital_image_processing/resize/__init__.py b/digital_image_processing/resize/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/digital_image_processing/resize/resize.py b/digital_image_processing/resize/resize.py
new file mode 100644
index 000000000..b7d493e70
--- /dev/null
+++ b/digital_image_processing/resize/resize.py
@@ -0,0 +1,69 @@
+""" Multiple image resizing techniques """
+import numpy as np
+from cv2 import imread, imshow, waitKey, destroyAllWindows
+
+
+class NearestNeighbour:
+    """
+    Simplest and fastest version of image resizing.
+    Source: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
+    """
+
+    def __init__(self, img, dst_width: int, dst_height: int):
+        if dst_width < 0 or dst_height < 0:
+            raise ValueError(f"Destination width/height should be > 0")
+
+        self.img = img
+        self.src_w = img.shape[1]
+        self.src_h = img.shape[0]
+        self.dst_w = dst_width
+        self.dst_h = dst_height
+
+        self.ratio_x = self.src_w / self.dst_w
+        self.ratio_y = self.src_h / self.dst_h
+
+        self.output = self.output_img = (
+            np.ones((self.dst_h, self.dst_w, 3), np.uint8) * 255
+        )
+
+    def process(self):
+        for i in range(self.dst_h):
+            for j in range(self.dst_w):
+                self.output[i][j] = self.img[self.get_y(i)][self.get_x(j)]
+
+    def get_x(self, x: int) -> int:
+        """
+        Get parent X coordinate for destination X
+        :param x: Destination X coordinate
+        :return: Parent X coordinate based on `x ratio`
+        >>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg", 1), 100, 100)
+        >>> nn.ratio_x = 0.5
+        >>> nn.get_x(4)
+        2
+        """
+        return int(self.ratio_x * x)
+
+    def get_y(self, y: int) -> int:
+        """
+        Get parent Y coordinate for destination Y
+        :param y: Destination X coordinate
+        :return: Parent X coordinate based on `y ratio`
+        >>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg", 1), 100, 100)
+        >>> nn.ratio_y = 0.5
+        >>> nn.get_y(4)
+        2
+        """
+        return int(self.ratio_y * y)
+
+
+if __name__ == "__main__":
+    dst_w, dst_h = 800, 600
+    im = imread("image_data/lena.jpg", 1)
+    n = NearestNeighbour(im, dst_w, dst_h)
+    n.process()
+
+    imshow(
+        f"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
+    )
+    waitKey(0)
+    destroyAllWindows()
diff --git a/digital_image_processing/test_digital_image_processing.py b/digital_image_processing/test_digital_image_processing.py
index 89cf6007a..327e2c67f 100644
--- a/digital_image_processing/test_digital_image_processing.py
+++ b/digital_image_processing/test_digital_image_processing.py
@@ -11,6 +11,7 @@ import digital_image_processing.change_contrast as cc
 import digital_image_processing.convert_to_negative as cn
 import digital_image_processing.sepia as sp
 import digital_image_processing.dithering.burkes as bs
+import digital_image_processing.resize.resize as rs
 from cv2 import imread, cvtColor, COLOR_BGR2GRAY
 from numpy import array, uint8
 from PIL import Image
@@ -82,3 +83,10 @@ def test_burkes(file_path: str = "digital_image_processing/image_data/lena_small
     burkes = bs.Burkes(imread(file_path, 1), 120)
     burkes.process()
     assert burkes.output_img.any()
+
+def test_nearest_neighbour(
+    file_path: str = "digital_image_processing/image_data/lena_small.jpg",
+):
+    nn = rs.NearestNeighbour(imread(file_path, 1), 400, 200)
+    nn.process()
+    assert nn.output.any()