Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning
文献类型：期刊论文
| 作者 | Kui Jiang; Ruoxi Wang; Yi Xiao; Junjun Jiang; Xin Xu; Tao Lu
|
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2024 |
| 卷号 | 11期号:11页码:2253-2269 |
| 关键词 | Association learning attention mechanism image enhancement perturbation modeling |
| ISSN号 | 2329-9266 |
| DOI | 10.1109/JAS.2024.124521 |
| 英文摘要 | Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network (PerTeRNet). It contains two sub-networks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery, we develop a novel perturbation-guided texture enhancement module (PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://github.com/kuijiang94/PerTeRNet. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/59451]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 GB/T 7714 | Kui Jiang,Ruoxi Wang,Yi Xiao,et al. Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning[J]. IEEE/CAA Journal of Automatica Sinica,2024,11(11):2253-2269. |
| APA | Kui Jiang,Ruoxi Wang,Yi Xiao,Junjun Jiang,Xin Xu,&Tao Lu.(2024).Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning.IEEE/CAA Journal of Automatica Sinica,11(11),2253-2269. |
| MLA | Kui Jiang,et al."Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning".IEEE/CAA Journal of Automatica Sinica 11.11(2024):2253-2269. |
入库方式： OAI收割
来源：自动化研究所
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