Automatic detection of well sampled images via a new ringing measure

Abstract : According to Shannon Sampling Theory, Fourier interpolation is the optimal way to reach subpixel accuracy from a properly-sampled digital image. However, for most images this interpolation tends to produce an artifact called ringing, that consists in undesirable oscillations near objects contours. In this work, we propose a way to detect this ringing artifact. Using Euler zigzag numbers, we compute the probability that neighboring gray-levels form an alternating sequence by chance, and characterize these undesirable ringing blocks as structures that would be very unlikely in a random image. We then show two applications where the associated algorithm is used to test or enforce the compliance of an image with Fourier interpolation.
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Communication dans un congrès
2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2010, Dallas, United States. pp.1030 - 1033, 2010, 〈10.1109/ICASSP.2010.5495324〉
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Gwendoline Blanchet, Lionel Moisan, Bernard Rougé. Automatic detection of well sampled images via a new ringing measure. 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2010, Dallas, United States. pp.1030 - 1033, 2010, 〈10.1109/ICASSP.2010.5495324〉. 〈hal-00388032v2〉

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