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A multiscale dilated residual network for image denoising

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Abstract

In this paper, a more effective Gaussian denoiser is designed to enhance the resulting image quality. We propose a novel image denoising method using a multiscale dilated residual network, named MDRNet. The proposed method is based on two main strategies. First, we adopt dilated convolutions in our network to enlarge the receptive field while requiring fewer parameters. The hybrid dilation rate pattern (HDP) is implemented such that each pixel in the pattern contributes similarly to the receptive field, allowing our network to learn the image details equally. Second, we employ a contextualized structure to take advantage of the low-level features which are mainly concentrated in the first two layers. Our method achieves competitive denoising performance and requires fewer parameters compared to existing denoising methods that using convolutional network. Through comprehensive experiments, we show that the denoising performance of our method is competitive with the state-of-the-art methods in terms of both quantitative and qualitative evaluation.

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Funding

This research was sponsered by the Research on the Major Scientific Instrument of National Natural Science Foundation of China (61727809).

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Authors and Affiliations

  1. Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, 96 Jinzhai road Baohe District, Hefei, Anhui, 230026, People’s Republic of China

    Dongjie Li, Huaian Chen, Guoqiang Jin, Yi Jin & Changan Zhu

  2. Department of Computer science, University of Science and Technology of China, 96 Jinzhai road Baohe District, Hefei, Anhui, 230026, People’s Republic of China

    Enhong Chen

Authors
  1. Dongjie Li
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  2. Huaian Chen
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  3. Guoqiang Jin
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  4. Yi Jin
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  6. Enhong Chen
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Correspondence to Yi Jin.

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Li, D., Chen, H., Jin, G. et al. A multiscale dilated residual network for image denoising. Multimed Tools Appl 79, 34443–34458 (2020). https://doi.org/10.1007/s11042-020-09113-z

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  • DOI: https://doi.org/10.1007/s11042-020-09113-z

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