Learning deep edge prior for image denoising

Image restoration is an important technique to deal with the degradation of the image. This paper presents an efficient and trusty denoising scheme, which combines the convolutional neural network (CNN) technique with the traditional variational model, to offer interpretable and high quality reconstructions. In this scheme, CNN, which has proven effectiveness in feature extraction tasks, is adopted to obtain the designed edge features from the noisy images, to be the prior of the reconstruction through an edge regularization. In the proposed denoising model, the total variation (TV) regularization is also adopted for its superior performance in allowing the sharp edges. The solution of the proposed model is obtained by using the Bregman splitting method, with the existence and the uniqueness of the solution also analyzed in this paper. Extensive experiments show that the two regularizations combined in the proposed model are able to fix the staircasing defects effectively and retrieve the fine textures in the recovered images as well, which outperforms the state-of-the-art interpretable denoising methods. Moreover, the proposed edge regularization can be easily extended into other kinds of noise or other restoration tasks, which implies the strong adaptivity of the proposed scheme.