SAR image de-noising based on texture strength and weighted nuclear norm minimization
Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China, Fang Jing, Liu Shuaiqi, School of Physics and Electronics, Shandong Normal University, Jinan 250014, China, College of Electronic and Information Engineering, Hebei University, Baoding 071000, China;, Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China, Xiao Chen Yang · Journal of Systems Engineering and Electronics · 2016
As synthetic aperture radar (SAR) has been widely used nearly in every field, SAR image de-noising became a very important research field. A new SAR image de-noising method based on texture strength and weighted nuclear norm minimization (WNNM) is proposed. To implement blind de-noising, the accurate estimation of noise variance is very important. So far, it is still a challenge to estimate SAR image noise level accurately because of the rich texture. Principal component analysis (PCA) and the low rank patches selected by image texture strength are used to estimate the noise level. With the help of noise level, WNNM can be expected to SAR image de-noising. Experimental results show that the proposed method outperforms many excellent de-noising algorithms such as Bayes least squares-Gaussian scale mixtures (BLS-GSM) method, non-local means (NLM) filtering in terms of both quantitative measure and visual perception quality.