Name
Abstract | ||
|---|---|---|
Conventional digital display devices, due to their hardware limitations, can't represent the whole range of luminance in High Dynamic Range (HDR) images. In order to solve this incompatible problem, many tone mapping techniques were introduced to reproduce HDR images. Unlike the traditional methods applied in standard scale space, this paper proposes a novel affine invariant features-based tone mapping algorithm in affine Gaussian scale space. The reason of using this scale space is due to the fact that it is able to extract anisotropic feature regions in addition to traditional isotropic feature regions. Firstly, the proposed method extracts the anisotropic features from HDR images and reforms them to be isotropic by Fitting & Affine transformation. Then, dodging-and-burning processing is utilized to obtain base layer of HDR images. Finally, two-scale edge-preserving decomposition is employed to generate detail layer of HDR images and combine two layers to produce output images. Experimental results show that the proposed algorithm outperforms previous methods for reproducing the real scene of HDR images, especially for large perspective and scale transformed images which contain considerable anisotropic feature regions. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SMC.2014.6974281 | SMC |
Keywords | DocType | ISSN |
dodging-and-burning,affine invariant feature-based tone mapping algorithm,anisotropic feature regions,luminance,image resolution,affine Gaussian scale space,tone mapping techniques,HDR images,high dynamic range images,two-scale image decomposition,two-scale edge-preserving decomposition,isotropic feature regions,tone mapping,feature extraction,edge detection,Gaussian processes,digital display devices,anisotropic feature extraction,affine transforms,fitting & affine transformation,dodging-and-burning processing | Conference | 1062-922X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiaosong Chen | 1 | 3 | 2.42 |
| Hai Li | 2 | 2435 | 208.37 |
| Yuanyuan Ding | 3 | 303 | 15.04 |
| Chang Liu | 4 | 159 | 52.61 |
| Jin Wang | 5 | 3 | 1.74 |
| Xin Deng | 6 | 2 | 1.37 |