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Deep CNN Denoiser prior for multiplicative noise removal

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Abstract

Multiplicative noise removal is always a hard problem in fundamental image processing task. Many methods are proposed for the multiplicative noise removal by using different denoiser prior in variational framework. Among the image prior, total variation (TV) is first proposed and then many other regularization such as PM, TGV, nonlocal and many other priors are also proposed for enhancing the denoising ability. Although using the priors can get good performance, the models are hard to be resolved with sophisticated priors. A new model based on the deep CNN denoiser prior for removing multiplicative noise is proposed in this paper. The proposed energy function is easy calculated via several sub-optimal questions by split bregman method and alternative minimization is used for the solution. The proposed method does not need to deduce the sophisticated formula and can achieve good performance. From the experiments, we can see that our method achieved good results.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No.61772294), The National Scientific and Technological Development and Planning Project during the Twelfth Five-year Plan Period (No.2014BAG03B05).

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Authors and Affiliations

  1. College of Computer Science and Technology, Qingdao University, Qingdao, China

    Guodong Wang & Zhenkuan Pan

  2. College of Data Science and Software Engineering, Qingdao University, Qingdao, China

    Zhimei Zhang

Authors
  1. Guodong Wang
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  2. Zhenkuan Pan
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  3. Zhimei Zhang
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Corresponding author

Correspondence to Guodong Wang.

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Wang, G., Pan, Z. & Zhang, Z. Deep CNN Denoiser prior for multiplicative noise removal. Multimed Tools Appl 78, 29007–29019 (2019). https://doi.org/10.1007/s11042-018-6294-9

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  • DOI: https://doi.org/10.1007/s11042-018-6294-9

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