LAUSR

Contouring artifacts usually appear in large and smooth flat areas, which are caused by many widely used processes such as bit-depth expansion, compression, image sharpening and contrast enhancement. Unfortunately, recent decontouring methods were mainly designed for specific and non-blind degradations, which significantly reduces the generalization ability of these methods when applied to complex and various real-world false contours. Therefore, this paper explores the blind decontouring problem by proposing a blind decontouring network (BDCN). Instead of directly training a decontouring network with mixed degradations, the proposed model consists of two independent modules, i.e., a flat region detection module (FDM) and a decontouring module (DCM). The FDM is designed to extract flat region masks robust to various false contours, which can preserve texture details from global smoothing. Then, the task of DCM becomes simply smoothing different contouring artifacts. Both the FDM and DCM are designed with a lightweight architecture and reparameterization strategy. Experimental results on both synthetic and real-world contouring artifacts demonstrate the effectiveness and generalization of the proposed method.