Fast and accurate image denoising via a deep convolutional-pairs network

Most of popular image denoising approaches exploit either the internal priors or the priors learned from external clean images to reconstruct the latent image. However, it is hard for those algorithms to construct the perfect connections between the clean images and the noisy ones. To tackle this problem, we present a deep convolutional-pairs network (DCPN) for image denoising in this paper. With the observation that deeper networks improve denoising performance, we propose to use deeper networks than those employed previously for low-level image processing tasks. In our method, we attempt to build end-to-end mappings directly from a noisy image to its corresponding noise-free image by using deep convolutional layers in pair applied to image patches. Because of those mappings trained from large data, the process of denoising is much faster than other methods. DCPN is composed of three convolutional-pairs layers and one transitional layer. Two convolutional-pairs layers are used for encoding and the other one is used for decoding. Numerical experiments show that the proposed method outperforms many state-of-the-art denoising algorithms in both speed and performance. © Springer International Publishing AG 2016.