Name
Abstract | ||
|---|---|---|
As one of the most fundamental operations in computer graphics and computer vision, sharpness enhancement can enhance an image in respect of sharpness characteristics. Unfortunately, the prevalent methods often fail to eliminate image noise, unrealistic details, or incoherent enhancement. In this paper, we propose a new sharpness enhancement approach that can boost the sharpness characteristics of an image effectively with affinity-based edge preserving. Our approach includes three gradient-domain operations: sharpness saliency representation, affinity-based gradient transformation, and gradient-domain image reconstruction. Moreover, we also propose an evaluation method based on sharpness distribution for analyzing all sharpness enhancement approaches in respect of sharpness characteristics. By evaluating the sharpness distribution and comparing the visual appearance, we demonstrate the effectiveness of our sharpness enhancement approach. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s00371-011-0668-6 | The Visual Computer |
Keywords | Field | DocType |
Sharpness enhancement, Gradient-domain filter, Image sharpening | Iterative reconstruction,Computer vision,Mathematical optimization,Computer science,Salience (neuroscience),Image noise,Artificial intelligence,Computer graphics,Visual appearance | Journal |
Volume | Issue | ISSN |
28 | 12 | 1432-2315 |
Citations | PageRank | References |
6 | 0.45 | 25 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhifeng Xie | 1 | 53 | 10.70 |
| Rynson W. H. Lau | 2 | 2015 | 176.56 |
| Yan Gui | 3 | 6 | 0.45 |
| Mingang Chen | 4 | 7 | 2.18 |
| Lizhuang Ma | 5 | 498 | 100.70 |