CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task
文献类型：预印本
| 作者 | Yang, Kangzhen3; Hu, Tao3; Dai, Kexin3; Chen, Genggeng2; Cao, Yu1; Dong, Wei2; Wu, Peng3; Zhang, Yanning3; Yan, Qingsen3 |
| 英文摘要 | 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. |
| 出处 | arXiv |
| 出版者 | arXiv |
| ISSN号 | 23318422 |
| 发表日期 | 2024-04-22 |
| 语种 | 英语 |
| 产权排序 | 3 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/97472]  |
| 专题 | 西安光学精密机械研究所_光电测量技术实验室 |
| 作者单位 | 1.Xi'an Institute of Optics and Precision Mechanics of CAS, China 2.Xi'an University of Architecture and Technology, China; 3.Northwestern Polytechnical University, 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. |
入库方式： OAI收割
来源：西安光学精密机械研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。