Local sparse representation for astronomical image denoising  

作　　者：杨阿锋 鲁敏 滕书华 孙即祥 

机构地区：[1]School of Electronic Science and Engineering,National University of Defense Technology

出　　处：《Journal of Central South University》2013年第10期2720-2727,共8页中南大学学报（英文版）

基　　金：Project(60972114) supported by the National Natural Science Foundation of China;Project(2012M512168) supported by China Postdoctoral Science Foundation

摘　　要：Motivated by local coordinate coding(LCC) theory in nonlinear manifold learning, a new image representation model called local sparse representation(LSR) for astronomical image denoising was proposed. Borrowing ideas from surrogate function and applying the iterative shrinkage-thresholding algorithm(ISTA), an iterative shrinkage operator for LSR was derived. Meanwhile, a fast approximated LSR method by first performing a K-nearest-neighbor search and then solving a l1optimization problem was presented under the guarantee of denoising performance. In addition, the LSR model and adaptive dictionary learning were incorporated into a unified optimization framework, which explicitly established the inner connection of them. Such processing allows us to simultaneously update sparse coding vectors and the dictionary by alternating optimization method. The experimental results show that the proposed method is superior to the traditional denoising method and reaches state-of-the-art performance on astronomical image.Motivated by local coordinate coding(LCC) theory in nonlinear manifold learning, a new image representation model called local sparse representation(LSR) for astronomical image denoising was proposed. Borrowing ideas from surrogate function and applying the iterative shrinkage-thresholding algorithm(ISTA), an iterative shrinkage operator for LSR was derived. Meanwhile, a fast approximated LSR method by first performing a K-nearest-neighbor search and then solving a l1optimization problem was presented under the guarantee of denoising performance. In addition, the LSR model and adaptive dictionary learning were incorporated into a unified optimization framework, which explicitly established the inner connection of them. Such processing allows us to simultaneously update sparse coding vectors and the dictionary by alternating optimization method. The experimental results show that the proposed method is superior to the traditional denoising method and reaches state-of-the-art performance on astronomical image.

关 键 词：astronomical image DENOISING LOCAL SPARSE representation(LSR) DICTIONARY learning ALTERNATING optimization 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]