First PRNU distance test

2024-03-22 11:58:34 +01:00
parent 92ede944c7
commit 452dd755fc
4 changed files with 30 additions and 17 deletions
--- a/algorithms/context_adaptive_interpolator/context_adaptive_interpolator.py
+++ b/algorithms/context_adaptive_interpolator/context_adaptive_interpolator.py
@@ -0,0 +1,56 @@
+# Based on https://web.archive.org/web/20231116015653/http://nrl.northumbria.ac.uk/id/eprint/29339/1/Paper_accepted.pdf IV. B..
+
+from PIL import Image
+from statistics import mean, median
+from wiener_filter import wienerFilter
+
+# Assume greyscale PIL image passed.
+# What about other color channels? See #11.
+def contextAdaptiveInterpolator(I, IImage, showProgress = False):
+    rImage = Image.new('L', (IImage.size[0] - 2, IImage.size[1] - 2))
+    r = rImage.load()
+
+    # This threshold is debatable. See #13.
+    THRESHOLD = 20
+
+    if showProgress:
+        print('before for loops')
+    # Equation (10)
+    # 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]
+                se = I[m + 1, n + 1]
+                s = I[m + 1, n]
+                sw = I[m + 1, n - 1]
+                w = I[m, n - 1]
+                nw = I[m - 1, n - 1]
+                no = I[m - 1, n]
+                ne = I[m - 1, n + 1]
+                A = [e, se, s, sw, w, nw, no, ne]
+                if max(A) - min(A) <= THRESHOLD:
+                    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 - 1, n - 1] = round(newPixel)
+    if showProgress:
+        print('after for loops')
+
+    # Why need to rotate the image? See #14.
+    #rImage.rotate(-90).show()
+
+    Q = 3
+    # $\sigma_0^2$ is the noise variance.
+    sigma_0 = 9 ** 0.5
+
+    if showProgress:
+        print('before wiener filter')
+    return wienerFilter(r, rImage, Q, sigma_0, showProgress)