Blind Multi-frame Super Resolution with Non-identical Blur

Real world video super resolution is an challenging problem due to the complex motion field and unknown blur kernel. Although multi-frame super resolution has been extensively studied in past decades, it still remained problems and always assumed that the blur kernels were identical in different frames. In this paper, we propose an novel blind multi-frame super resolution method with non-identical blur. To estimate blur kernels of different frames, we propose using salient edges selection method for more accurate kernel estimation. The whole process of estimation is based on Hyper-Laplacian prior, and iterative value updating through a multi-scale process. After the kernels of different frames are estimated, the high resolution frame is reconstructed using a cost function. The proposed method can obtain superior results, and outperforms the state of the art in the experiments through subjective and objective evaluation.