Image Denoising Algorithm Based on Method Noise Sparse Representation Dictionary Learning

Abstract

Abstract: For the shortcoming of losting partial texture information with image denoising process, the image denoising algorithm based on method noise sparse representation was proposed. The method noise, which was defined as the difference between the noisy and the denoised image, was obtained by guided filter. Then redundant dictionary was learned by improved dictionary learning method and the method noise. The image texture information in method noise was extracted by the learning dictionary, and image was restored by the extracted image texture information and denoised image by guided filter. The experimental results demonstrate that the peak signal to noise ratio Value (PSNR) of the proposed algorithm is better than state-of-the-art algorithms, while the proposed algorithm can well preserve the texture information in the denoised image, making them look more natural.

Key words: method noise, dictionary learning, redundant dictionary, sparse representation

CLC Number:

P391.41

Huang Lishao, Wen Haiying, Gu Sisi. Image Denoising Algorithm Based on Method Noise Sparse Representation Dictionary Learning[J]. Journal of System Simulation, 2016, 28(1): 154-159.

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