BM3D-Net: A Convolutional Neural Network for Transform-Domain Collaborative Filtering

Dong Yang, Jian Sun

Abstract: 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.