Local sparse representation for astronomical image denoising

• Published in: Journal of Central South University Vol. 20; no. 10; pp. 2720 - 2727
• Main Authors: Yang, A-feng, Lu, Min, Teng, Shu-hua, Sun, Ji-xiang
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2013
• Subjects: Engineering; Metallic Materials; astronomical image denoising; dictionary learning; alternating optimization; local sparse representation (LSR)
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-013-1789-z

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 l 1 optimization 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.