Spatially varying regularization of image sequences super-resolution

Authors:
Yaozu An;Yao Lu;Zhengang Zhai
Affiliations:
Beijing Laboratory of Intelligent Information Technology, School of Computer, Beijing Institute of Technology, Beijing, China;Beijing Laboratory of Intelligent Information Technology, School of Computer, Beijing Institute of Technology, Beijing, China;Beijing Laboratory of Intelligent Information Technology, School of Computer, Beijing Institute of Technology, Beijing, China
Venue:
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Year:
2009

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Abstract

This paper presents a spatially varying super-resolution approach that estimates a high-resolution image from the low-resolution image sequences and better removes Gaussian additive noise with high variance Firstly, a spatially varying functional in terms of local mean residual is used to weight each low-resolution channel Secondly, a newly adaptive regularization functional based on the spatially varying residual is determined within each low-resolution channel instead of the overall regularization parameter, which balances the prior term and fidelity residual term at each iteration Experimental results indicate the obvious performance improvement in both PSNR and visual effect compared to non-channel-weighted method and overall-channel-weighted method.