Speckle Noise Removal in SAR Images based on Sparse Coding by Dictionary Learning and Collaborative Filtering

Recently, dictionaries combined with sparse learning techniques became extremely popular in computer vision and image processing. Three basic approaches to image denoising are spatial domain method, transform domain method and dictionary learning method. Generally, dictionary learning is based on learning a large group of image patches in such a way that each patch in the output image is expressed as a linear combination of few atoms of the dictionary. This paper presents a method based on dictionary learning and collaborative filtering to despeckle Synthetic Aperture Radar (SAR) image. In this paper, we present a comparative result among different dictionary learning algorithms based on DCT, K-SVD and BM3D applied on the Synthetic Aperture Radar (SAR) despeckling task. The experimental results show that the proposed K-SVD algorithm is provide an adequate results in removing speckle noise from the SAR images. Keywords— SAR, Sparse Representation, Dictionary Learning, K-SVD, BM3D