Deep unfolding multi-scale regularizer network for image denoising
文献类型：期刊论文
| 作者 | Xu, Jingzhao3; Yuan, Mengke1,2 ; Yan, Dong-Ming1,2; Wu, Tieru3
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| 刊名 | COMPUTATIONAL VISUAL MEDIA
 |
| 出版日期 | 2023-06-01 |
| 卷号 | 9期号:2页码:335-350 |
| 关键词 | image denoising deep unfolding network multi-scale regularizer deep learning |
| ISSN号 | 2096-0433 |
| DOI | 10.1007/s41095-022-0277-5 |
| 通讯作者 | Wu, Tieru(wutr@jlu.edu.cn) |
| 英文摘要 | Existing deep unfolding methods unroll an optimization algorithm with a fixed number of steps, and utilize convolutional neural networks (CNNs) to learn data-driven priors. However, their performance is limited for two main reasons. Firstly, priors learned in deep feature space need to be converted to the image space at each iteration step, which limits the depth of CNNs and prevents CNNs from exploiting contextual information. Secondly, existing methods only learn deep priors at the single full-resolution scale, so ignore the benefits of multi-scale context in dealing with high level noise. To address these issues, we explicitly consider the image denoising process in the deep feature space and propose the deep unfolding multi-scale regularizer network (DUMRN) for image denoising. The core of DUMRN is the feature-based denoising module (FDM) that directly removes noise in the deep feature space. In each FDM, we construct a multi-scale regularizer block to learn deep prior information from multi-resolution features. We build the DUMRN by stacking a sequence of FDMs and train it in an end-to-end manner. Experimental results on synthetic and real-world benchmarks demonstrate that DUMRN performs favorably compared to state-of-the-art methods. |
| WOS关键词 | NONLOCAL IMAGE ; SPARSE |
| 资助项目 | National Key R&D Program of China ; National Nature Science Foundation of China ; [2020YFA0714101] ; [61872162] ; [62102414] ; [62172415] ; [52175493] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000907570700008 |
| 出版者 | SPRINGERNATURE |
| 资助机构 | National Key R&D Program of China ; National Nature Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51120]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Wu, Tieru |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Jilin Univ, Sch Math, Changchun 130012, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Jingzhao,Yuan, Mengke,Yan, Dong-Ming,et al. Deep unfolding multi-scale regularizer network for image denoising[J]. COMPUTATIONAL VISUAL MEDIA,2023,9(2):335-350. |
| APA | Xu, Jingzhao,Yuan, Mengke,Yan, Dong-Ming,&Wu, Tieru.(2023).Deep unfolding multi-scale regularizer network for image denoising.COMPUTATIONAL VISUAL MEDIA,9(2),335-350. |
| MLA | Xu, Jingzhao,et al."Deep unfolding multi-scale regularizer network for image denoising".COMPUTATIONAL VISUAL MEDIA 9.2(2023):335-350. |
入库方式： OAI收割
来源：自动化研究所
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