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20 February 2012 Foveated self-similarity in nonlocal image filtering
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Proceedings Volume 8291, Human Vision and Electronic Imaging XVII; 829110 (2012)
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States
Nonlocal image filters suppress noise and other distortions by searching for similar patches at different locations within the image, thus exploiting the self-similarity present in natural images. This similarity is typically assessed by a windowed distance of the patches pixels. Inspired by the human visual system, we introduce a patch foveation operator and measure patch similarity through a foveated distance, where each patch is blurred with spatially variant point-spread functions having standard deviation increasing with the spatial distance from the patch center. In this way, we install a different form of self-similarity in images: the foveated self-similarity. We consider the Nonlocal Means algorithm (NL-means) for the removal of additive white Gaussian noise as a simple prototype of nonlocal image filtering and derive an explicit construction of its corresponding foveation operator, thus yielding the Foveated NL-means algorithm. Our analysis and experimental study show that, to the purpose of image denoising, the foveated self-similarity can be a far more effective regularity assumption than the conventional windowed self-similarity. In the comparison with NL-means, the proposed foveated algorithm achieves a substantial improvement in denoising quality, according to both objective criteria and visual appearance, particularly due to better contrast and sharpness. Moreover, foveation is introduced at a negligible cost in terms of computational complexity.
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Alessandro Foi and Giacomo Boracchi "Foveated self-similarity in nonlocal image filtering", Proc. SPIE 8291, Human Vision and Electronic Imaging XVII, 829110 (20 February 2012);

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