57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
# Based on https://web.archive.org/web/20231116015653/http://nrl.northumbria.ac.uk/id/eprint/29339/1/Paper_accepted.pdf IV. B..
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from PIL import Image
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from statistics import mean, median
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from wiener_filter import wienerFilter
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# Assume greyscale PIL image passed.
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# What about other color channels? See #11.
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# `THRESHOLD` seems to have been designed to assume 256 range based images.
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def contextAdaptiveInterpolator(I, IImage, showProgress = False):
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rImage = Image.new('L', (IImage.size[0] - 2, IImage.size[1] - 2))
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r = rImage.load()
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# This threshold is debatable. See #13.
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THRESHOLD = 20
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if showProgress:
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print('before for loops')
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# Equation (10)
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# Accelerate computation. See #15.
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for m in range(1, IImage.size[0] - 1):
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for n in range(1, IImage.size[1] - 1):
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e = I[m , n + 1]
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se = I[m + 1, n + 1]
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s = I[m + 1, n]
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sw = I[m + 1, n - 1]
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w = I[m , n - 1]
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nw = I[m - 1, n - 1]
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no = I[m - 1, n]
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ne = I[m - 1, n + 1]
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A = [e, se, s, sw, w, nw, no, ne]
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if max(A) - min(A) <= THRESHOLD:
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newPixel = I[m, n] - mean(A)
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elif abs(e - w) - abs(no - s) > THRESHOLD:
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newPixel = I[m, n] - (s + no) / 2
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elif abs(s - no) - abs(e - w) > THRESHOLD:
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newPixel = I[m, n] - (e + w) / 2
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elif abs(sw - ne) - abs(se - nw) > THRESHOLD:
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newPixel = I[m, n] - (se + nw) / 2
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elif abs(se - nw) - abs(sw - ne) > THRESHOLD:
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newPixel = I[m, n] - (sw + ne) / 2
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else:
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newPixel = I[m, n] - median(A)
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r[m - 1, n - 1] = round(newPixel)
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if showProgress:
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print('after for loops')
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# Why need to rotate the image? See #14.
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#rImage.rotate(-90).show()
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Q = 3
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# $\sigma_0^2$ is the noise variance.
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sigma_0 = 9 ** 0.5
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if showProgress:
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print('before wiener filter')
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return wienerFilter(r, rImage, Q, sigma_0, showProgress) |