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
Hauptverfasser: Zhang, Jun, Tan, Yaoxin, Wei, Xiaohui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.02.2025
Schlagworte:
High-order correlation
High-order structure
Hypergraph convolutional network
Hyperspectral image denoising
Hyperspectral image denoising
High-order structure
High-order correlation
Hypergraph convolutional network
ISSN:0165-1684
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Zusammenfassung: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.
ISSN:0165-1684
DOI:10.1016/j.sigpro.2024.109718