access icon free Image denoising based on sparse representation and gradient histogram

© The Institution of Engineering and Technology
Received 19/02/2016, Accepted 03/09/2016, Revised 09/08/2016, Published 14/09/2016

Various image priors, such as sparsity prior, non-local self-similarity prior and gradient histogram prior, have been widely used for noise removal, while preserving the image texture. However, the gradient histogram prior used for texture enhancement sometimes generates false textures in the smooth areas. In order to address these problems, the authors propose a robust algorithm combining gradient histogram with sparse representation to obtain good estimates of the sparse coding coefficients of the latent image and realising image denoising while preserving the texture. The proposed model is solved by having a balance between over-enhancement and over-smoothing of the texture in order to preserve the natural texture appearance. Experimental results demonstrate the efficiency and effectiveness of the proposed method.

Inspec keywords: image denoising; image representation; image texture

Other keywords: texture enhancement; image priors; sparse representation; image texture; natural texture appearance; image denoising; sparsity prior; latent image; gradient histogram prior; non-local self-similarity prior; sparse coding coefficients; false textures; noise removal

Subjects: Computer vision and image processing techniques; Optical, image and video signal processing

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