Abstract

Image super-resolution (SR) reconstruction is one of the most popular research topics in image processing for decades. This paper presents a novel approach to deal with single image SR problem. We search a mapping between a pair of low-resolution and high-resolution image patch in gradient domain by learning a generic image database and the input image itself. Given low-resolution image, the high-resolution image is reconstructed using sparse representation in gradient domain and solving Poisson equation. Experiments demonstrate that the state-of-the-art results have been achieved compared to other SR methods in terms of both PSNR and visual perception.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.