Elastic net with adaptive weight for image denoising

Jun Xiao; Rui Zhao; Kin-Man Lam

doi:10.1117/12.2566961

1 June 2020 Elastic net with adaptive weight for image denoising

Jun Xiao, Rui Zhao, Kin-Man Lam

Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 1151506 (2020) https://doi.org/10.1117/12.2566961
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia

Abstract

Sparse models have been widely used in image denoising, and have achieved state-of-the-art performance in past years. Dictionary learning and sparse code estimation are the two key issues for sparse models. When a dictionary is learned, sparse code estimation is equivalent to a general least absolute shrinkage and selection operator (LASSO) problem. However, there are two limitations of LASSO: 1). LASSO gives rise to a biased estimation. 2). LASSO cannot select highly correlated features simultaneously. In recent years, methods for dictionary construction based on the nonlocal self-similarity property and weighted sparse model, relying on noise estimation, have been proposed. These methods can reduce the biased gap of the estimation, and thus achieve promising results for image denoising. In this paper, we propose an elastic net with adaptive weight for image denoising. Our proposed model can achieve nearly unbiased estimation and select highly correlated features. Experimental results show that our proposed method outperforms other state-of-the-art image denoising methods.

Citation Download Citation

Jun Xiao, Rui Zhao, and Kin-Man Lam "Elastic net with adaptive weight for image denoising", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 1151506 (1 June 2020); https://doi.org/10.1117/12.2566961

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KEYWORDS

Image denoising

Associative arrays

Performance modeling

Denoising

RGB color model

Data modeling

Image restoration

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