Variational reconstruction and restoration for video Super-Resolution

This paper presents a variational framework for obtaining super-resolved video-sequences, based on the observation that reconstruction-based Super-Resolution (SR) algorithms are limited by two factors: registration exactitude and Point Spread Function (PSF) estimation accuracy. To minimize the impact of the first limiting factor, a small-scale linear in-painting algorithm is proposed to provide smooth SR video frames. To improve the second limiting factor, a fast PSF local estimation and total variation-based denoising is proposed. Experimental results reflect the improvements provided by the proposed method when compared to classic SR approaches.