期刊
IEEE SIGNAL PROCESSING LETTERS
卷 25, 期 1, 页码 55-59出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2017.2768660
关键词
BM3D; convolutional neural networks; denoising; nonlocal methods
资金
- National Science Foundation of China [11622106, 61472313]
Denoising is a fundamental task in image processing with wide applications for enhancing image qualities. BM3D is considered as an effective baseline for image denoising. Although learning-based methods have been dominant in this area recently, the traditional methods are still valuable to inspire new ideas by combining with learning-based approaches. In this letter, we propose a new convolutional neural network inspired by the classical BM3D algorithm, dubbed as BM3D-Net. We unroll the computational pipeline of BM3D algorithm into a convolutional neural network structure, with extraction and aggregation layers to model block matching stage in BM3D. We apply our network to three denoising tasks: gray-scale image denoising, color image denoising, and depth map denoising. Experiments show that BM3D-Net significantly outperforms the basic BM3D method, and achieves competitive results compared with state of the art on these tasks.
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