Open AccessCombination of geometric clustering and nonlocal means for SAR image despeckling
Author(s) -
Feng Wensen,
Lei Hong
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2013.2755
Subject(s) - synthetic aperture radar , artificial intelligence , computer science , similarity (geometry) , filter (signal processing) , cluster analysis , image (mathematics) , measure (data warehouse) , probabilistic logic , speckle noise , noise (video) , pattern recognition (psychology) , similarity measure , computer vision , data mining
Recently, nonlocal filtering methods for synthetic aperture radar (SAR) despeckling have attracted a lot of attention. In general, a suitable similarity measure well suited to SAR images is derived by incorporating the noise statistics. One important nonlocal framework is the probabilistic patch‐based (PPB) filter, which derives the similarity measure in a data‐driven way and provides promising results. A drawback of this filter is the suppression of thin and dark details since the PPB method takes into account the photometrically similar patches, yet it ignores the geometric structure of image patches. To overcome these disadvantages, a new patch‐based despeckling method is presented which exploits both geometrical and photometrical similarities. Numerical experiments suggest that the proposed method is on a par with or exceeds the state‐of‐the‐art PPB method, both visually and quantitatively.