Image denoising via deep residual convolutional neural networks


Recently, convolutional neural network (CNN)-based methods have achieved impressive performance on image denoising. Notably, CNN with deeper and thinner structures is more flexible to extract the image details. However, direct stacking some existing networks is difficult to achieve satisfactory denoising performance. In this paper, we propose a novel deep residual convolutional neural network (DRCNN) for image denoising. The main structure of DRCNN is the residual block that consists of two convolutional layers, and there are skip connections between these two convolutional layers without the batch normalization operation. The skip connection not only directly transfers the input image information to the hidden layer but also reduces the path length of gradient transfer, making the gradient transfer in a short path and alleviating the vanishing-gradient problem. DRCNN is compared with several state-of-the-art algorithms, and the experimental results demonstrated its denoising effectiveness.

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This work was partially supported by the National Natural Science Foundation of China (Nos. 61702129, 61772149, 61762028, and U1701267), China Postdoctoral Science Foundation (No. 2018M633047), and Guangxi Science and Technology Project (Nos. AD18216004, AD18281079, and 2018GNSFAA138132).

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Correspondence to Cheng Pang.

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Lan, R., Zou, H., Pang, C. et al. Image denoising via deep residual convolutional neural networks. SIViP 15, 1–8 (2021).

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  • Image denoising
  • Convolutional neural network
  • Residual learning