| CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task | |
| Yang, Kangzhen1; Hu, Tao1; Dai, Kexin1; Chen, Genggeng2; Cao, Yu3; Dong, Wei2; Wu, Peng1; Zhang, Yanning1; Yan, Qingsen1 | |
| 作者部门 | 光电跟踪与测量技术研究室 |
| 2024-04-22 | |
| 出处 | arXiv |
| ISSN | 23318422 |
| 产权排序 | 3 |
| 摘要 | In real-world scenarios, images captured often suffer from blurring, noise, and other forms of image degradation, and due to sensor limitations, people usually can only obtain low dynamic range images. To achieve high-quality images, researchers have attempted various image restoration and enhancement operations on photographs, including denoising, deblurring, and high dynamic range imaging. However, merely performing a single type of image enhancement still cannot yield satisfactory images. In this paper, to deal with the challenge above, we propose the Composite Refinement Network (CRNet) to address this issue using multiple exposure images. By fully integrating information-rich multiple exposure inputs, CRNet can perform unified image restoration and enhancement. To improve the quality of image details, CRNet explicitly separates and strengthens high and low-frequency information through pooling layers, using specially designed Multi-Branch Blocks for effective fusion of these frequencies. To increase the receptive field and fully integrate input features, CRNet employs the High-Frequency Enhancement Module, which includes large kernel convolutions and an inverted bottleneck ConvFFN. Our model secured third place in the first track of the Bracketing Image Restoration and Enhancement Challenge, surpassing previous SOTA models in both testing metrics and visual quality. Copyright © 2024, The Authors. All rights reserved. |
| 收录类别 | EI |
| 语种 | 英语 |
| 出版者 | arXiv |
| EI入藏号 | 20240193624 |
| 文献类型 | 预印本 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/97472 |
| 专题 | 光电跟踪与测量技术研究室 |
| 作者单位 | 1.Northwestern Polytechnical University, China; 2.Xi'an University of Architecture and Technology, China; 3.Xi'an Institute of Optics and Precision Mechanics of CAS, China |
| 推荐引用方式 GB/T 7714 | Yang, Kangzhen,Hu, Tao,Dai, Kexin,et al. CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task. 2024. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| CRNet A Detail-Prese（5921KB） | 预印本 | 限制开放 | CC BY-NC-SA | 请求全文 | ||
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