An Improved SAR Image Denoising Method Based on Bootstrap Statistical Estimation with ICA Basis. Issue 4 (1st July 2016)

• Record Type: Journal Article
• Authors: Ji, Jian; Li, Yang
• Is Part Of: Chinese journal of electronics. Volume 25:Issue 4(2016)
• Page Start: 786
• Page End: 792
• Publication Date: 2016-07-01
• Subjects: Synthetic aperture radar (SAR) -- Image denoising -- Independent component analysis (ICA) basis -- Bootstrap -- Maximum a posterior probability (MAP); adaptive filters -- image denoising -- independent component analysis -- maximum likelihood estimation -- radar imaging -- synthetic aperture radar; improved SAR image denoising method -- bootstrap statistical estimation -- ICA basis -- synthetic aperture radar image denoising -- independent component analysis -- speckle statistical model -- noise signal subspace -- maximum a posterior probability filter -- MAP filter -- adaptive filtering; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2016.06.040
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : A new method for Synthetic aperture radar (SAR) image denoising is proposed. The prior information of speckle statistical model can be exploited to judge its distribution. The basis of SAR image can be estimated by Independent component analysis (ICA), and these bases can be divided into two different subspaces (noise and real signal subspaces) through a linear classifier. Then parametric Bootstrap estimates the parameters of speckle statistical model on the noise signal subspace, and the nonparametric Bootstrap can estimate the distribution of real image on the real signal subspace. According to different results estimated by Bootstrap, corresponding Maximum a posterior probability (MAP) filter will be selected for image denoising, using the noise model's parameter for adaptive filtering. Experiments show that the image processed by this new method can achieve a better visual perception and objective evaluation results.