Abstract
The recent nonlocal means filter is a very successful technique for denoising textured images. In this paper, we formulate a variational technique that leads to an adaptive version of this filter. In particular, in an iterative manner, the filtering result is employed to redefine the similarity of patches in the next iteration. We further introduce the idea to replace the neighborhood weighting by a sorting criterion. This addresses the parameter selection problem of the original nonlocal means filter and leads to favorable denoising results of textured images, particularly in case of large noise levels.
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Brox, T., Cremers, D. (2007). Iterated Nonlocal Means for Texture Restoration. In: Sgallari, F., Murli, A., Paragios, N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2007. Lecture Notes in Computer Science, vol 4485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72823-8_2
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DOI: https://doi.org/10.1007/978-3-540-72823-8_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72822-1
Online ISBN: 978-3-540-72823-8
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