Name
Abstract | ||
|---|---|---|
We propose a class of enhancement techniques suitable for scenes captured by fixed cameras. The basic idea is to increase the information density in a set of low quality images by extracting the context from a higher-quality image captured under different illuminations from the same viewpoint. For example, a night-time surveillance video can be enriched with information available in daytime images. We also propose a new image fusion approach to combine images with sufficiently different appearance into a seamless rendering. Our method ensures the fidelity of important features and robustly incorporates background contexts, while avoiding traditional problems such as aliasing, ghosting and haloing. We show results on indoor as well as outdoor scenes. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1142/S0218001405004137 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
image enhancement, video enhancement, gradient domain integration | Information density,Computer vision,Fidelity,Image fusion,Computer graphics (images),Aliasing,Artificial intelligence,Rendering (computer graphics),Mathematics,Ghosting | Journal |
Volume | Issue | ISSN |
19 | 4 | 0218-0014 |
Citations | PageRank | References |
9 | 0.72 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adrian Ilie | 1 | 166 | 15.20 |
| Ramesh Raskar | 2 | 5305 | 422.69 |
| Jingyi Yu | 3 | 1238 | 101.25 |