Name
Abstract | ||
|---|---|---|
Degraded low-resolution (LR) images are often obtained from cameras. Resolution enhancement and image restoration are very practical in many fields such as medical imaging, surveillance system and remote sensing. Single image super-resolution is a technique which reconstruct a restored high-resolution (HR) image from a degraded LR image. In this paper, we propose single image super-resolution based on sparse coding using self-similarity prior. A sparsity constraint is used to jointly train coupled dictionaries which can generate high frequency details. Reconstructed HR output is enhanced with non-local means based on self-similarity prior. Experimental results demonstrate that our method shows higher performance than other existing algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCE.2019.8662051 | 2019 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE) |
Field | DocType | ISSN |
Computer vision,Medical imaging,Neural coding,Computer science,Artificial intelligence,Image restoration,Superresolution,Self-similarity | Conference | 2158-3994 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoojun Nam | 1 | 0 | 0.68 |
| junwon mun | 2 | 0 | 2.03 |
| Yunseok Jang | 3 | 1 | 1.36 |
| Jaeseok Kim | 4 | 405 | 58.33 |