Nonlocal similarity image filtering

Yifei Lou, Paolo Favaro, Stefano Soatto, Andrea Bertozzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Citations (Scopus)


We exploit the recurrence of structures at different locations, orientations and scales in an image to perform denoising. While previous methods based on "nonlocal filtering" identify corresponding patches only up to translations, we consider more general similarity transformations. Due to the additional computational burden, we break the problem down into two steps: First, we extract similarity invariant descriptors at each pixel location; second, we search for similar patches by matching descriptors. The descriptors used are inspired by scale-invariant feature transform (SIFT), whereas the similarity search is solved via the minimization of a cost function adapted from local denoising methods. Our method compares favorably with existing denoising algorithms as tested on several datasets. © 2009 Springer Berlin Heidelberg.

Original languageEnglish
Title of host publicationImage Analysis and Processing - ICIAP 2009 - 15th International Conference, Proceedings
Number of pages10
Volume5716 LNCS
Publication statusPublished - 2009
Event15th International Conference on Image Analysis and Processing - Vietri sul Mare, Italy
Duration: 8 Sept 200911 Sept 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5716 LNCS
ISSN (Print)0302-9743


Conference15th International Conference on Image Analysis and Processing
Abbreviated title ICIAP 2009
CityVietri sul Mare


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