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

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