Name
Title | ||
|---|---|---|
Restricted Boltzmann machine approach to couple dictionary training for image super-resolution |
Abstract | ||
|---|---|---|
Image super-resolution means forming high-resolution images from low-resolution images. In this paper, we develop a new approach based on the deep Restricted Boltzmann Machines (RBM) for image super-resolution. The RBM architecture has ability of learning a set of visual patterns, called dictionary elements from a set of training images. The learned dictionary will be then used to synthesize high resolution images. We test the proposed algorithm on both benchmark and natural images, comparing with several other techniques. The visual quality of the results has also been assessed by both human evaluation and quantitative measurement. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICIP.2013.6738103 | ICIP |
Keywords | Field | DocType |
restricted boltzmann machine approach,rbm architecture,boltzmann machines,sparse modelling,natural images,learning (artificial intelligence),image resolution,dictionaries,visual patterns,training images,dictionary training,dictionary elements,restricted boltzmann machine,dictionary learning,image super-resolution,high-resolution image,learning artificial intelligence | Computer vision,Restricted Boltzmann machine,Boltzmann machine,Pattern recognition,Computer science,Artificial intelligence,Superresolution,Image resolution,Visual patterns | Conference |
ISSN | Citations | PageRank |
1522-4880 | 5 | 0.48 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junbin Gao | 1 | 1112 | 119.67 |
| Yi Guo | 2 | 55 | 8.79 |
| Ming Yin | 3 | 51 | 5.90 |