Multi-wavelet residual dense convolutional neural network for image denoising

• URL: http://arxiv.org/abs/2002.08301v1
• Date: Wed, 19 Feb 2020 17:21:37 GMT
• Title: Multi-wavelet residual dense convolutional neural network for image denoising
• Authors: Shuo-Fei Wang, Wen-Kai Yu, and Ya-Xin Li
• Abstract summary: We use the short-term residual learning method to improve the performance and robustness of networks for image denoising tasks. Here, we choose a multi-wavelet convolutional neural network (MWCNN) as the backbone, and insert residual dense blocks (RDBs) in its each layer. Compared with other RDB-based networks, it can extract more features of the object from adjacent layers, preserve the large RF, and boost the computing efficiency.
• Score: 2.500475462213752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Networks with large receptive field (RF) have shown advanced fitting ability in recent years. In this work, we utilize the short-term residual learning method to improve the performance and robustness of networks for image denoising tasks. Here, we choose a multi-wavelet convolutional neural network (MWCNN), one of the state-of-art networks with large RF, as the backbone, and insert residual dense blocks (RDBs) in its each layer. We call this scheme multi-wavelet residual dense convolutional neural network (MWRDCNN). Compared with other RDB-based networks, it can extract more features of the object from adjacent layers, preserve the large RF, and boost the computing efficiency. Meanwhile, this approach also provides a possibility of absorbing advantages of multiple architectures in a single network without conflicts. The performance of the proposed method has been demonstrated in extensive experiments with a comparison with existing techniques.

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