Exploring high-order correlation for hyperspectral image denoising with hypergraph convolutional network
High-order correlation is an important property of hyperspectral images (HSIs) and has been widely investigated in model-based HSI denoising. However, the existing deep learning-based HSI denoising approaches have not fully utilized the high-order correlation. Hypergraph convolutional networks have...
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| Veröffentlicht in: | Signal processing Jg. 227; S. 109718 |
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| Abstract | High-order correlation is an important property of hyperspectral images (HSIs) and has been widely investigated in model-based HSI denoising. However, the existing deep learning-based HSI denoising approaches have not fully utilized the high-order correlation. Hypergraph convolutional networks have shown great potential in capturing the high-order correlation. Therefore, in this paper, we propose a novel HSI denoising method by employing hypergraph convolution to characterize the high-order correlation at the patch level. Specifically, our framework is a symmetrically skip-connected 3D encoder–decoder architecture, which enhances the extraction and utilization of local features. Furthermore, to integrate competently the hypergraph convolutional modules into the 3D framework, we devise a dimensional transformation module that facilitates the fusion of 3D convolution and hypergraph convolution. Notably, in the hypergraph convolution operation, we use a data-driven technique to acquire the incidence matrix of a hypergraph, efficiently constructing the HSI into a high-order structure. Our proposed method excels in HSI denoising performance compared to state-of-the-art approaches, evidenced by extensive experiments on synthetic and real-world noisy HSIs.
•We propose a novel HSI denoising method based on HGCN.•We present a learning-based technique to acquire the incidence matrix of a hypergraph.•The structure and complexity analysis of the proposed model are discussed.•Our proposed method excels in HSI denoising performance compared to state-of-the-art approaches. |
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| AbstractList | High-order correlation is an important property of hyperspectral images (HSIs) and has been widely investigated in model-based HSI denoising. However, the existing deep learning-based HSI denoising approaches have not fully utilized the high-order correlation. Hypergraph convolutional networks have shown great potential in capturing the high-order correlation. Therefore, in this paper, we propose a novel HSI denoising method by employing hypergraph convolution to characterize the high-order correlation at the patch level. Specifically, our framework is a symmetrically skip-connected 3D encoder–decoder architecture, which enhances the extraction and utilization of local features. Furthermore, to integrate competently the hypergraph convolutional modules into the 3D framework, we devise a dimensional transformation module that facilitates the fusion of 3D convolution and hypergraph convolution. Notably, in the hypergraph convolution operation, we use a data-driven technique to acquire the incidence matrix of a hypergraph, efficiently constructing the HSI into a high-order structure. Our proposed method excels in HSI denoising performance compared to state-of-the-art approaches, evidenced by extensive experiments on synthetic and real-world noisy HSIs.
•We propose a novel HSI denoising method based on HGCN.•We present a learning-based technique to acquire the incidence matrix of a hypergraph.•The structure and complexity analysis of the proposed model are discussed.•Our proposed method excels in HSI denoising performance compared to state-of-the-art approaches. |
| ArticleNumber | 109718 |
| Author | Tan, Yaoxin Zhang, Jun Wei, Xiaohui |
| Author_xml | – sequence: 1 givenname: Jun orcidid: 0000-0003-3809-7023 surname: Zhang fullname: Zhang, Jun email: junzhang0805@126.com organization: Jiangxi Province Key Laboratory of Smart Water Conservancy, Nanchang Institute of Technology, Nanchang 330099, Jiangxi, China – sequence: 2 givenname: Yaoxin surname: Tan fullname: Tan, Yaoxin email: tyaoxin@163.com organization: Jiangxi Province Key Laboratory of Smart Water Conservancy, Nanchang Institute of Technology, Nanchang 330099, Jiangxi, China – sequence: 3 givenname: Xiaohui surname: Wei fullname: Wei, Xiaohui email: xhweei@hunnu.edu.cn organization: College of Information Science and Engineering, Hunan Normal University, Changsha 410081, Hunan, China |
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| Cites_doi | 10.1109/TGRS.2015.2452812 10.1109/CVPR.2016.207 10.1109/CVPR.2018.00262 10.1109/TGRS.2018.2859203 10.1109/TIP.2015.2496263 10.1109/TIP.2021.3138325 10.1145/3605776 10.1109/TGRS.2020.3045273 10.1109/ICCV51070.2023.01201 10.1109/TNNLS.2020.2978756 10.3390/rs15030694 10.1109/JSTARS.2012.2232904 10.1109/TGRS.2019.2897316 10.3390/rs14184598 10.1109/TGRS.2013.2284280 10.1109/TIP.2012.2210725 10.1109/TGRS.2018.2865197 10.1109/CVPR.2014.377 10.1109/TGRS.2023.3328922 10.1109/TGRS.2019.2901737 10.1109/TCYB.2017.2677944 10.1609/aaai.v33i01.33013558 10.1109/CVPR.2019.00703 10.1109/TIP.2003.819861 10.1109/TGRS.2014.2301415 10.1109/TGRS.2015.2457614 10.1016/j.neucom.2018.10.023 10.1109/TIP.2015.2393057 10.1109/TIP.2020.3013166 10.1109/JSTARS.2017.2779539 10.1109/TGRS.2022.3172371 10.1016/j.neucom.2022.01.057 10.1016/j.neucom.2020.04.138 10.1609/aaai.v37i1.25221 10.1109/TGRS.2022.3227735 10.1109/TPAMI.2023.3241756 10.1109/CVPR.2017.625 10.1109/CVPR52688.2022.01716 10.1109/TGRS.2019.2946050 10.1109/CVPR52729.2023.00562 10.3390/rs70202046 10.1109/TGRS.2012.2185054 10.1109/TCI.2019.2911881 10.1109/TGRS.2014.2321557 |
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| Keywords | Hyperspectral image denoising High-order structure High-order correlation Hypergraph convolutional network |
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