WIP: Getting PRNU by averaging on fake dataset

algorithms/context_adaptive_interpolator/context_adaptive_interpolator.py

@@ -19,11 +19,11 @@ def contextAdaptiveInterpolator(I, IImage, showProgress = False):
     # Accelerate computation. See #15.
     for m in range(1, IImage.size[0] - 1):
         for n in range(1, IImage.size[1] - 1):
-                e = I[m, n + 1]
+                e  = I[m    , n + 1]
                 se = I[m + 1, n + 1]
-                s = I[m + 1, n]
+                s  = I[m + 1, n]
                 sw = I[m + 1, n - 1]
-                w = I[m, n - 1]
+                w  = I[m    , n - 1]
                 nw = I[m - 1, n - 1]
                 no = I[m - 1, n]
                 ne = I[m - 1, n + 1]

algorithms/context_adaptive_interpolator/wiener_filter.py

@@ -5,7 +5,6 @@ def wienerFilter(r, rImage, Q, sigma_0, showProgress):
     h_wImage = Image.new('L', (rImage.size[0], rImage.size[1]))
     h_wImagePixels = h_wImage.load()
 
-    # Wiener filter.
     def h_w(hImage, h, i, j):
         # Equation (7)
         return h[i, j] * sigma(hImage, h, i, j) / (sigma(hImage, h, i, j) + sigma_0 ** 2)