Abstract
Super-resolution (SR) is the problem of generating a high-resolution (HR) image from one or more low-resolution (LR) images. This study presents a new approach to single-image super-resolution based on group sparse representation. Two dictionaries are constructed corresponding to the LR and HR image patches, respectively. The sparse coefficients of an input LR image patch in terms of the LR dictionary are used to recover the HR patch from the HR dictionary. When constructing the dictionaries, the three colour channels in a training image patch are considered a group composed of three atoms. The whole group is selected simultaneously when representing an image patch so that the correlations between the colour channels can be retained. A dictionary training method is also designed in which the two dictionaries are trained jointly to ensure that the corresponding LR and HR patches have the same sparse coefficients. Experimental results demonstrate the effectiveness of the proposed method and its robustness to noise.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.