Diffusion-Steered Super-Resolution Image
| dc.contributor.author | Maiseli, Baraka J. | |
| dc.date.accessioned | 2019-05-05T09:29:43Z | |
| dc.date.available | 2019-05-05T09:29:43Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | For decades, super-resolution has been a widely applied technique to improve the spatial resolution of an image without hardware modification. Despite the advantages, super-resolution suffers from ill-posedness, a problem that makes the technique susceptible to multiple solutions. Therefore, scholars have proposed regularization approaches as attempts to address the challenge. The present work introduces a parameterized diffusion-steered regularization framework that integrates total variation (TV) and Perona-Malik (PM) smoothing functionals into the classical super-resolution model. The goal is to establish an automatic interplay between TV and PM regularizers such that only their critical useful properties are extracted to well pose the super-resolution problem, and hence, to generate reliable and appreciable results. Extensive analysis of the proposed resolution-enhancement model shows that it can respond well on different image regions. Experimental results provide further evidence that the proposed model outperforms. | en_US |
| dc.identifier.issn | 0192303X | |
| dc.identifier.uri | http://hdl.handle.net/20.500.11810/5194 | |
| dc.language.iso | en | en_US |
| dc.publisher | IntechOpen | en_US |
| dc.relation.ispartofseries | DOI;10.5772/intechopen.71024 | |
| dc.subject | super-resolution | en_US |
| dc.subject | resolution | en_US |
| dc.subject | enhancement | en_US |
| dc.subject | regularization | en_US |
| dc.subject | diffusion | en_US |
| dc.title | Diffusion-Steered Super-Resolution Image | en_US |
| dc.type | Book chapter | en_US |