CFNet: Conditional filter learning with dynamic noise estimation for real image denoising
文献类型：期刊论文
| 作者 | Zuo, Yifan3; Yao, Wenhao3; Zeng, Yifeng2; Xie, Jiacheng3; Fang, Yuming3; Huang, Yan1; Jiang, Wenhui3 |
| 刊名 | KNOWLEDGE-BASED SYSTEMS
 |
| 出版日期 | 2024-01-25 |
| 卷号 | 284页码:12 |
| ISSN号 | 0950-7051 |
| 关键词 | Image denoising Noise estimation Conditional filter Affine transform |
| DOI | 10.1016/j.knosys.2023.111320 |
| 通讯作者 | Fang, Yuming(fa0001ng@e.ntu.edu.sg) |
| 英文摘要 | A mainstream type of the state of the arts (SOTAs) based on convolutional neural network (CNN) for real image denoising contains two sub-problems, i.e., noise estimation and non-blind denoising. This paper considers real noise approximated by heteroscedastic Gaussian/Poisson-Gaussian distributions with in-camera signal processing pipelines. The related works always exploit the estimated noise prior via channel-wise concatenation followed by a convolutional layer with spatially sharing kernels. Due to the variable modes of noise strength and frequency details of all feature positions, this design cannot adaptively tune the corresponding denoising patterns. To address this problem, we propose a novel conditional filter in which the optimal kernels for different feature positions can be adaptively inferred by local features from the image and the noise map. Also, we bring the thought that alternatively performs noise estimation and non-blind denoising into CNN structure, which continuously updates noise prior to guide the iterative feature denoising. In addition, according to the property of heteroscedastic Gaussian distribution, a novel affine transform block is designed to predict the stationary noise component and the signal-dependent noise component. Compared with SOTAs, extensive experiments are conducted on five synthetic datasets and four real datasets, which shows the improvement of the proposed CFNet. The code and models are available via https://github.com/WenhaoYao/CFNet/. |
| WOS关键词 | NETWORK |
| 资助项目 | National Natural Science Foundation of China[62271237] ; National Natural Science Foundation of China[62132006] ; Natural Science Foundation of Jiangxi Province[20224ACB212005] ; Natural Science Foundation of Jiangxi Province[20223AEI91002] ; Natural Science Foundation of Jiangxi Province[20224BAB212010] ; Double Thousand Plan of Jiangxi Province[jxsq2019101076] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:001149545300001 |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Jiangxi Province ; Double Thousand Plan of Jiangxi Province |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55446]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Fang, Yuming |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Northumbria Univ, Dept Comp & Informat Sci, 110 Middlesex St,330031, London, England 3.Jiangxi Univ Finance & Econ, Sch Informat Management, 665 Yuping West St, Nanchang 330031, Jiangxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zuo, Yifan,Yao, Wenhao,Zeng, Yifeng,et al. CFNet: Conditional filter learning with dynamic noise estimation for real image denoising[J]. KNOWLEDGE-BASED SYSTEMS,2024,284:12. |
| APA | Zuo, Yifan.,Yao, Wenhao.,Zeng, Yifeng.,Xie, Jiacheng.,Fang, Yuming.,...&Jiang, Wenhui.(2024).CFNet: Conditional filter learning with dynamic noise estimation for real image denoising.KNOWLEDGE-BASED SYSTEMS,284,12. |
| MLA | Zuo, Yifan,et al."CFNet: Conditional filter learning with dynamic noise estimation for real image denoising".KNOWLEDGE-BASED SYSTEMS 284(2024):12. |
入库方式： OAI收割
来源：自动化研究所
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