External prior learning and internal mean sparse coding for image denoising

Qiongshuai Lyu; Min Guo; Miao Ma; Richard Mankin

doi:10.1117/1.JEI.28.3.033014

23 May 2019 External prior learning and internal mean sparse coding for image denoising

Qiongshuai Lyu, Min Guo, Miao Ma, Richard Mankin

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 3, 033014 (May 2019). https://doi.org/10.1117/1.JEI.28.3.033014

Abstract

Image prior and sparse coding learning methods have important uses in image denoising. Many denoising methods learn priors either from the noisy image itself or an external clean image dataset. But using only these as priors does not always reconstruct the image effectively. In addition, when the image is corrupted by noise, the local sparse coding coefficient obtained from a noisy image patch is inaccurate, restricting denoising performance. We present a noise removal framework based on external prior learning and an internal mean sparse coding method, making use of the innate sparsity and nonlocal self-similarity (NSS) of natural images. Specifically, we first obtain external priors from a clean natural image dataset by Gaussian mixture model. The external priors are applied to guide the subspace clustering of internal noisy image patches, and a compact dictionary is generated for each internal noisy patch cluster. Then an internal mean sparse coding strategy based on NSS is introduced into the sparse representation model, whose regularization parameters then are deduced through a Bayesian framework. An iterative shrinkage method is employed to solve the l₁-optimization problem in the sparse representation model. Application of the noise removal model to 16 test images demonstrates denoising performance exceeding other competing methods.

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Qiongshuai Lyu, Min Guo, Miao Ma, and Richard Mankin "External prior learning and internal mean sparse coding for image denoising," Journal of Electronic Imaging 28(3), 033014 (23 May 2019). https://doi.org/10.1117/1.JEI.28.3.033014

Received: 4 November 2018; Accepted: 1 May 2019; Published: 23 May 2019

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