You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
20 February 2012Foveated self-similarity in nonlocal image filtering
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.
The alert did not successfully save. Please try again later.
Alessandro Foi, Giacomo Boracchi, "Foveated self-similarity in nonlocal image filtering," Proc. SPIE 8291, Human Vision and Electronic Imaging XVII, 829110 (20 February 2012); https://doi.org/10.1117/12.912217