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A salt and pepper noise image denoising method based on the generative classification

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Abstract

In this paper, an image denoising algorithm is proposed for salt and pepper noise. First, a generative model is built on a patch as a basic unit and then the algorithm locates the image noise within that patch in order to better describe the patch and obtain better subsequent clustering. Second, the algorithm classifies patches using a generative clustering method, which provides additional similarity information for noise repairing, suppresses the interference of noise and abandons those classes that consist of a smaller number of patches. Finally, the algorithm builds a non-local switching filter to remove the salt and pepper noise. Simulation results show that the proposed algorithm effectively denoises salt and pepper noise of various densities. It obtains a better visual quality and higher peak signal-to-noise ratio score than several state-of-the-art algorithms. In short, our algorithm uses a noisy patch as the basic unit, a patch clustering method to optimize the repair data set as well as obtains a better denoising effect, and provides a guideline for future denoising and repair methods.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (NSFC) Grant No. 61702246, 61402214, 61602204, and 41671439, Liaoning Province of China General Project of Scientific Research No. L2015285, Liaoning Province of China Doctoral Research Fund No. 201601243, and Liaoning University Youth Project No.LS2014L014.

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Authors and Affiliations

  1. College of Computer and Information Technology, Liaoning Normal University, Dalian, China

    Bo Fu, **aoyang Zhao, Chuanming Song & **anghai Wang

  2. College of Computer Science and Technology, Jilin University, Changchun, China

    **ming Li

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  1. Bo Fu
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  2. **aoyang Zhao
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  3. Chuanming Song
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  4. **ming Li
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  5. **anghai Wang
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Correspondence to Bo Fu.

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Fu, B., Zhao, X., Song, C. et al. A salt and pepper noise image denoising method based on the generative classification. Multimed Tools Appl 78, 12043–12053 (2019). https://doi.org/10.1007/s11042-018-6732-8

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