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Mixed Noise Removal by Bilateral Weighted Sparse Representation

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Abstract

Image denoising is a fundamental but difficult task in image processing. Recovering a clean version from an image corrupted by mixed Gaussian and impulse noise is still challenging. The weighted sparse representation (WSR) method has been applied to deal with mixed noise and has achieved good performance. However, the WSR model presents an obvious disadvantage due to oversmoothing of the texture regions. In this paper, the novel bilateral weighted sparse representation model is presented for mixed noise removal. By introducing an image gradient-based weight, an adaptive sparse ratio is provided for each image patch to protect additional image details. In addition, a weighted method noise regularization term is proposed to utilize the global image information submerged in the method noise, which can improve the visual effect of recovered noise-free images. Both the subjective and objective performance are evaluated, and the experimental results show that the proposed method outperforms existing mixed noise removal algorithms, especially in terms of the visual performance.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported by the Science and Technology Major Project of Anhui Province of China under Grant 2019b05050001 and the National Natural Science Foundation of China under Grant 61805065.

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Authors and Affiliations

  1. School of Instrumentation Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei, 230009, Anhui, China

    Jiechao Sheng & Guoqiang Lv

  2. National Engineering Laboratory of Special Display Technology, National Key Laboratory of Advanced Display Technology, Academy of Photoelectric Technology, Hefei University of Technology, Hefei, 230009, Anhui, China

    Jiechao Sheng, Guoqiang Lv, Zhitian Xue, Lei Wu & Qibin Feng

  3. School of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, 230009, Anhui, China

    Zhitian Xue & Lei Wu

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  1. Jiechao Sheng
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  2. Guoqiang Lv
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Correspondence to Qibin Feng.

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Sheng, J., Lv, G., Xue, Z. et al. Mixed Noise Removal by Bilateral Weighted Sparse Representation. Circuits Syst Signal Process 40, 4490–4515 (2021). https://doi.org/10.1007/s00034-021-01677-x

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