WIP: Context-Adaptive Interpolator (CAI)

algorithms/context-adaptive_interpolator.py

@@ -1,14 +1,17 @@
+# Based on https://web.archive.org/web/20231116015653/http://nrl.northumbria.ac.uk/id/eprint/29339/1/Paper_accepted.pdf
+
 from PIL import Image
-from statistics import mean
+from statistics import mean, median
 
 # What about other color channels?
 image = Image.open('9f04e2005fddb9d5512e2f42a3b826b019755717.jpg').convert('L')
-newI = image.load()
+r = image.load()
 I = image.copy().load()
 DEFAULT_COLOR = 255
 # This threshold is debatable.
 THRESHOLD = 20
 # How to manage the border of the image?
+# Equation (10)
 for m in range(1, image.size[0] - 1):
     for n in range(1, image.size[1] - 1):
             e = I[m, n + 1]
@@ -21,6 +24,30 @@ for m in range(1, image.size[0] - 1):
             ne = I[m - 1, n + 1]
             A = [e, se, s, sw, w, nw, no, ne]
             if max(A) - min(A) <= THRESHOLD:
-                newI[m, n] = int(round(I[m, n] - mean(A), 0))
+                newPixel = I[m, n] - mean(A)
+            elif abs(e - w) - abs(no - s) > THRESHOLD:
+                newPixel = I[m, n] - (s + no) / 2
+            elif abs(s - no) - abs(e - w) > THRESHOLD:
+                newPixel = I[m, n] - (e + w) / 2
+            elif abs(sw - ne) - abs(se - nw) > THRESHOLD:
+                newPixel = I[m, n] - (se + nw) / 2
+            elif abs(se - nw) - abs(sw - ne) > THRESHOLD:
+                newPixel = I[m, n] - (sw + ne) / 2
+            else:
+                newPixel = I[m, n] - median(A)
+            r[m, n] = round(newPixel)
+
+Q = 3
+
+def wienerFilter():
+    # Equation (7)
+    hw[i, j] = h[i, j] * sigma(i, j) / (sigma(i, j) + sigma_0 ** 2)
+
+def sigma(i, j):
+    # Equation (9)
+    return sigma_q(i, j, Q)
+
+def sigma_q(i, j, q):
+    # Equation
 
 image.rotate(-90).show()