Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/47612
Title: Supervised constrained optimization of bayesian nonlocal means filter with sigma preselection for despeckling SAR images
Authors: Gomez, Luis 
Munteanu, Cristian G.
Buemi, Maria E.
Jacobo-Berlles, Julio C.
Mejail, Marta E.
UNESCO Clasification: 220990 Tratamiento digital. Imágenes
Keywords: Interactive Evolution
Quality Assessment
Adaptation
Issue Date: 2013
Publisher: 0196-2892
Journal: IEEE Transactions on Geoscience and Remote Sensing 
Abstract: Speckle reduction is an important problem in synthetic aperture radar (SAR) image analysis. Recent years have seen how Bayesian filters emerge as the natural extension of the nonlocal means filters, providing a general framework to deal with multiplicative (speckle) noise. In this paper, we present an easy-to-use software tool applying an evolutionary algorithm to optimize a Bayesian nonlocal means filter with sigma preselection for denoising SAR images. The desired result is a filtered image having a significative reduction in its variance but preserving the original mean value of the noisy image. A mixed-integer constrained optimization problem is stated and solved with the human intervention, where the user assists the evolutionary algorithm to reduce the noisy image variance under the restriction of keeping the mean value of the noisy SAR image within a predetermined interval of acceptance. We apply the methodology to a set of synthetic and real SAR speckle corrupted images. The results through the evaluation of objective global and local quality criteria show the excellent potential of the proposal.
URI: http://hdl.handle.net/10553/47612
ISSN: 0196-2892
DOI: 10.1109/TGRS.2013.2269866
Source: IEEE Transactions on Geoscience and Remote Sensing[ISSN 0196-2892],v. 51 (6555865), p. 4563-4575
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