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A Multi-scale Dilated Residual Convolution Network for Image Denoising

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Abstract

Due to the excellent performance of deep learning, more and more image denoising methods based on convolutional neural networks (CNN) are proposed, including dilated convolution method and multi-scale convolution method. A fundamental issue is how to obtain multi-scale information and to recover the image detail. In order to solve the issue, we present a multi-scale dilated residual convolution network (MDRN), which has a multi-scale feature extraction block and dilated residual block. The multi-scale feature extraction block, making full of the multi-scale information, is presented by incorporating multiple-scale pixel shuffle downsampling, which can extract salient features from input images. At the same time, the dilated residual block expands the receptive field and can effectively utilize the global image information. Extensive experimental results on both the synthetic and real-world noisy images show that our method is effective and surpasses the state-of-the-art denoising methods in terms of both quantitative and qualitative evaluations.

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Notes

  1. https://www.eecs.yorku.ca/~kamel/sidd/benchmark.php.

  2. https://noise.visinf.tu-darmstadt.de/benchmark.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No.61873155), the National Natural Science Foundation of Shaanxi Province (No.2018JM6050).

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Authors and Affiliations

  1. Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, 710062, China

    Xinlei Jia, Yali Peng, Bao Ge, Shigang Liu & Wenan Wang

  2. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Xinlei Jia, Yali Peng, Shigang Liu & Wenan Wang

  3. School of Computer Science and Technology, Nanjing Normal University, Nanjing, 210046, China

    Jun Li

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  1. Xinlei Jia
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Correspondence to Yali Peng.

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Jia, X., Peng, Y., Ge, B. et al. A Multi-scale Dilated Residual Convolution Network for Image Denoising. Neural Process Lett 55, 1231–1246 (2023). https://doi.org/10.1007/s11063-022-10934-2

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