A bayesian approach to adaptive video super resolution

Although multi-frame super resolution has been exten-sively studied in past decades, super resolving real-world video sequences still remains challenging. In existing sys-tems, either the motion models are oversimplified, or impor-tant factors such as blur kernel and noise level are assumed to be known. Such models cannot deal with the scene and imaging conditions that vary from one sequence to another. In this paper, we propose a Bayesian approach to adaptive video super resolution via simultaneously estimating under-lying motion, blur kernel and noise level while reconstruct-ing the original high-res frames. As a result, our system not only produces very promising super resolution results that outperform the state of the art, but also adapts to a variety of noise levels and blur kernels. Theoretical analysis of the relationship between blur kernel, noise level and frequency-wise reconstruction rate is also provided, consistent with our experimental results. 1