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src/methods/high_quality_interpolation.py

+import numpy as np
+
+
+def is_green(i, j):
+    return (not i%2 and not j%2) or (i%2 and j%2)
+
+def is_red(i, j):
+    return not i%2 and j%2
+
+def is_blue(i, j):
+    return i%2 and not j%2
+
+
+g_ker = np.array([
+    [0, 0, -1, 0, 0],
+    [0, 0, 2, 0, 0],
+    [-1, 2, 4, 2, -1],
+    [0, 0, 2, 0, 0],
+    [0, 0, -1, 0, 0]
+]) / 8
+
+rgrrow_ker = np.array([
+    [0, 0, 0.5, 0, 0],
+    [0, -1, 0, -1, 0],
+    [-1, 4, 5, 4, -1],
+    [0, -1, 0, -1, 0],
+    [0, 0, 0.5, 0, 0]
+]) / 8
+
+rgrcol_ker = np.array([
+    [0, 0, -1, 0, 0],
+    [0, -1, 4, -1, 0],
+    [0.5, 0, 5, 0, 0.5],
+    [0, -1, 4, -1, 0],
+    [0, 0, -1, 0, 0]
+]) / 8
+
+rb_ker = np.array([
+    [0, 0, -1.5, 0, 0],
+    [0, 2, 0, 2, 0],
+    [-1.5, 0, 6, 0, -1.5],
+    [0, 2, 0, 2, 0],
+    [0, 0, -1.5, 0, 0]
+]) / 8
+
+
+
+def high_quality_interpolation(image):
+
+    res = np.empty((image.shape[0], image.shape[1], 3))
+    
+    estimated_green = np.zeros_like(image)
+    estimated_red = np.zeros_like(image)
+    estimated_blue = np.zeros_like(image)
+
+    for i in range(2, image.shape[0]-2):
+        for j in range(2, image.shape[1]-2):
+            if (is_red(i, j)):
+                estimated_red[i, j] = image[i, j]
+                estimated_green[i, j] = (image[i-2:i+3, j-2:j+3] * g_ker).sum()
+                estimated_blue[i, j] = (image[i-2:i+3, j-2:j+3] * rb_ker).sum()
+            elif (is_blue(i, j)):
+                estimated_red[i, j] = (image[i-2:i+3, j-2:j+3] * rb_ker).sum()
+                estimated_green[i, j] = (image[i-2:i+3, j-2:j+3] * g_ker).sum()
+                estimated_blue[i, j] = image[i, j]
+            else:
+                estimated_green[i, j] = image[i, j]
+                if (is_red(i-1, j)):
+                    estimated_red[i, j] = (image[i-2:i+3, j-2:j+3] * rgrcol_ker).sum()
+                    estimated_blue[i, j] = (image[i-2:i+3, j-2:j+3] * rgrrow_ker).sum()
+                else:
+                    estimated_red[i, j] = (image[i-2:i+3, j-2:j+3] * rgrrow_ker).sum()
+                    estimated_blue[i, j] = (image[i-2:i+3, j-2:j+3] * rgrcol_ker).sum()
+
+
+    res[:, :, 0] = estimated_red.clip(0, 1)
+    res[:, :, 1] = estimated_green.clip(0, 1)
+    res[:, :, 2] = estimated_blue.clip(0, 1)
+    
+    return res
+