Name
Abstract | ||
|---|---|---|
A novel divide-and-conquer strategy for image enhancement is proposed.The difference of image low-frequency and high-frequency is utilized.The different importance of low-frequency and high-frequency is exploited.This strategy is effective in image naturalization and details promotion. Existing enhancement methods tend to overlook the difference between image components of low-frequency and high-frequency. However, image low-frequency portions contain smooth areas occupied the majority of the image, while high-frequency components are sparser in the image. Meanwhile, the different importance of image low-frequency and high-frequency components cannot be precisely and effectively for image enhancement. Therefore, it is reasonable to deal with these components separately when designing enhancement algorithms with image subspaces. In this paper, we propose a novel divide-and-conquer strategy to decompose the observed image into four subspaces and enhance the images corresponding to each subspace individually. We employ the existing technique of gradient distribution specification for these enhancements, which has displayed promising results for image naturalization. We then reconstruct the full image using the weighted fusion of these four subspace images. Experimental results demonstrate the effectiveness of the proposed strategy in both image naturalization and details promotion. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.jvcir.2017.02.018 | J. Visual Communication and Image Representation |
Keywords | Field | DocType |
Image enhancement,Subspace decomposition,Gradient distribution specification,Weighted fusion | Top-hat transform,Anisotropic diffusion,Computer vision,Image warping,Subspace topology,Feature detection (computer vision),Pattern recognition,Image texture,Binary image,Artificial intelligence,Image restoration,Mathematics | Journal |
Volume | Issue | ISSN |
45 | C | 1047-3203 |
Citations | PageRank | References |
0 | 0.34 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peixian Zhuang | 1 | 14 | 7.39 |
| Xueyang Fu | 2 | 354 | 29.09 |
| Yue Huang | 3 | 317 | 29.82 |
| Xinghao Ding | 4 | 591 | 52.95 |