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: PDFPersönliche VDE-Mitglieder erhalten auf diesen Artikel 10% Rabatt
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.