A Mixed Non-local Prior Model for Image Super-resolution Reconstruction

Generating high-resolution image from a set of degraded low-resolution images is a challenge prob-lem in image processing. Due to the ill-posed nature of Super-resolution (SR), it is necessary to find an eff ective image prior model to make it well-posed. For this pur-pose, we propose a mixed non-local prior model by adap-tively combining the non-local total variation and non-local H1 models, and establish a multi-frame SR method based on this mixed non-local prior model. The unknown High-resolution (HR) image, motion parameters and hyper-parameters related to the new prior model and noise statis-tics are determined automatically, resulting in an unsu-pervised super-resolution method. Extensive experiments demonstrate the eff ectiveness of the proposed SR method, which can not only preserve image details better but also suppress noise better.