Abstract
Multi-frame image super-resolution (SR) is a procedure which takes several observed degraded low-resolution (LR) images, and processes them together to synthesize one or more high-quality high-resolution (HR) images. Due to an insufficient number of LR images and ill-conditioned blur operators, SR image reconstruction is a typical ill-posed inverse problem. However, the regularization approach can be used as an effective means to solve this kind of problems. In this paper, we propose an algorithm for multi-frame SR reconstruction with two new types of regularization terms, termed as local weighted anisotropy regularization and successive regularization toward iteration process, which are characterized by strong ability of suppressing noise and preserving edge information in HR image reconstruction. Furthermore, an iterative refinement procedure obtained superior result by employing Bregman iteration. Experiment results show that the effectiveness of the proposed regularization method and corresponding algorithm for SR reconstruction.
Sign-in/Register to access full text options 
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 callMore From: Optik - International Journal for Light and Electron Optics
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.
