Abstract
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.
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Foundation item: Project(60972114) supported by the National Natural Science Foundation of China; Project(2012M512168) supported by China Postdoctoral Science Foundation
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Yang, Af., Lu, M., Teng, Sh. et al. Local sparse representation for astronomical image denoising. J. Cent. South Univ. 20, 2720–2727 (2013). https://doi.org/10.1007/s11771-013-1789-z
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DOI: https://doi.org/10.1007/s11771-013-1789-z