Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation

Abstract

Multi-frame super-resolution makes up for the deficiency of sensor hardware and significantly improves image resolution by using the information of inter-frame and intra-frame images. Inaccurate blur kernel estimation will enlarge the distortion of the estimated high-resolution image. Therefore, multi-frame blind super resolution with unknown blur kernel is more challenging. For the purpose of reducing the impact of inaccurate motion estimation and blur kernel estimation on the super-resolved image, we propose a novel method combining motion estimation, blur kernel estimation and super resolution. The confidence weight of low-resolution images and the parameter value of the motion model obtained in image reconstruction are added to the modified motion estimation and blur kernel estimation. At the same time, Jacobian matrix, which can better describe the motion change, is introduced to further correct the error of motion estimation. Based on the results acquired from the experiments on synthetic data and real data, the superiority of the proposed method over others is obvious. The reconstructed high-resolution image retains the details of the image effectively, and the artifacts are greatly reduced.