Fast deblurring in video super resolution
Xueqing Yang, Tingting Fan · 2016
Multi-frame super resolution has been well studied in recent years, but blur kernel is always assumed to be known in video super resolution problem. Most blind deconvolution algorithm can both estimate the blur kernel and the sharp image. In this paper, we originally adopt a fast single image blind deconvolution algorithm in video super resolution to estimate high-resolution image and blur kernel. In super resolution part, we use a Bayesian framework to estimate motion, noise parameters and high-resolution image simultaneously. In blind deconvolution part, we use the first reconstructed high-resolution image as blurred image to restore the sharp image and blur kernel, and smooth sparse prior is used here. By introducing blind deconvolution, super resolution algorithm only needs several iteration to reach promising results.