Enhanced Image Decoding via Edge-Preserving Generative Adversarial Networks

Lossy image compression usually introduces undesired compression artifacts, such as blocking, ringing and blurry effect{###} S, especially in low bit rate coding scenarios. Although many algorithms have been proposed to reduce these compression artifacts, most of them are based on image local smoothness prior, which usually leads to over-smoothing around the areas with distinct structures, e.g., edges and textures. In this paper, we propose a novel framework to enhance the perceptual quality of decoded images by well preserving the edge structures and predicting visually pleasing textures. Firstly, we propose an edge-preserving generative adversarial network (EP-GAN) to achieve edge restoration and texture generation simultaneously. Then, we elaborately design an edge fidelity regularization term to guide our network, which jointly utilizes the signal fidelity, feature fidelity and adversarial constraint to reconstruct high quality decoded images. Experimental results demonstrate that the proposed EP-GAN is able to efficiently enhance decoded images at low bit rate and reconstruct more perceptually pleasing images with abundant textures and sharp edges.