SAR Despeckling by Sparse Reconstruction on Affinity Nets (SRAN)

Konferenz: EUSAR 2012 - 9th European Conference on Synthetic Aperture Radar
23.04.2012 - 26.04.2012 in Nuremberg, Germany

Tagungsband: EUSAR 2012

Seiten: 4Sprache: EnglischTyp: PDF

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Porikli, Fatih; Sundaresan, Rajagopalan (Mitsubishi Electric Research Labs, USA)
Suwa, Kei (Mitsubishi Electric Corporation, Japan)

This paper presents a new approach for multiplicative noise removal in SAR images based on sparse coding by dictionary learning and collaborative filtering. First, an affinity net is formed by clustering log-similar image patches where a cluster is represented as a node in the net. For each cluster, an under-complete dictionary is computed using the alternative decision method that iteratively updates the dictionary and the sparse coefficients. The nodes belonging to the same cluster are then reconstructed by a sparse combination of the corresponding dictionary atoms. The reconstructed patches are finally collaboratively aggregated to build the denoised image. Experimental results demonstrate superior despeckle filtering performance.