.

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
-