Name
Title | ||
|---|---|---|
Super-Resolution On Degraded Low-Resolution Images Using Convolutional Neural Networks |
Abstract | ||
|---|---|---|
Single Image Super-Resolution (SISR) has witnessed a dramatic improvement in recent years through the use of deep learning and, in particular, convolutional neural networks (CNN). In this work we address reconstruction from low-resolution images and consider as well degrading factors in images such as blurring. To address this challenging problem, we propose a new architecture to tackle blur with the down-sampling of images by extending the DBSRCNN architecture [1]. We validate our new architecture (DBSR) experimentally against several state of the art super-resolution techniques. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/EUSIPCO.2019.8903000 | 2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) |
Keywords | Field | DocType |
Image super-resolution, image deblurring, deep learning, CNN | Computer vision,Architecture,Convolutional neural network,Computer science,Artificial intelligence,Deep learning,Superresolution | Conference |
ISSN | Citations | PageRank |
2076-1465 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fatma Albluwi | 1 | 0 | 0.68 |
| Vladimir A. Krylov | 2 | 0 | 0.34 |
| Rozenn Dahyot | 3 | 340 | 32.62 |