MSFDN: multi-scale spatial-spectral-frequency joint denoising network for hyperspectral images
Hyperspectral images often suffer from various types of noise, such as Gaussian noise, impulse noise, stripe noise, and deadline, which are caused by weather conditions and sensor equipment. Hyperspectral denoising aims to remove noise and obtain clear images. Therefore, in order to more accurately...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-03-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2025.2477552 |
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| author | Kai Ren Weiwei Sun Xiangchao Meng Gang Yang |
| author_facet | Kai Ren Weiwei Sun Xiangchao Meng Gang Yang |
| author_sort | Kai Ren |
| collection | DOAJ |
| description | Hyperspectral images often suffer from various types of noise, such as Gaussian noise, impulse noise, stripe noise, and deadline, which are caused by weather conditions and sensor equipment. Hyperspectral denoising aims to remove noise and obtain clear images. Therefore, in order to more accurately describe and extract noise, and obtain better denoising results, we propose a novel approach called the multi-scale spatial-spectral-frequency joint denoising network for hyperspectral images (MSFDN). Considering the spatial-spectral distribution characteristics of noisy signals, MSFDN first converts the hyperspectral image into a low-dimensional subspace by constructing a subspace projection network. Then, the frequency distribution characteristics of noise signals in subspaces are described, and the noise signals are removed by designing a noise separation network. Finally, a subspace back projection network is constructed to reconstruct the initialized denoising image, and the conditional diffusion model is used to refine the initialized denoising results. It is worth noting that the above framework is embedded in the UNet network. Extensive experiments are conducted on simulated and real datasets. The experimental results demonstrate that MSFDN outperforms other state-of-the-art methods and exhibits robustness. |
| format | Article |
| id | doaj-art-7e3592bd8ca649bd853f2d6c6d71002d |
| institution | OA Journals |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-7e3592bd8ca649bd853f2d6c6d71002d2025-08-20T01:48:40ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532025-03-0112010.1080/10095020.2025.2477552MSFDN: multi-scale spatial-spectral-frequency joint denoising network for hyperspectral imagesKai Ren0Weiwei Sun1Xiangchao Meng2Gang Yang3Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo, ChinaHyperspectral images often suffer from various types of noise, such as Gaussian noise, impulse noise, stripe noise, and deadline, which are caused by weather conditions and sensor equipment. Hyperspectral denoising aims to remove noise and obtain clear images. Therefore, in order to more accurately describe and extract noise, and obtain better denoising results, we propose a novel approach called the multi-scale spatial-spectral-frequency joint denoising network for hyperspectral images (MSFDN). Considering the spatial-spectral distribution characteristics of noisy signals, MSFDN first converts the hyperspectral image into a low-dimensional subspace by constructing a subspace projection network. Then, the frequency distribution characteristics of noise signals in subspaces are described, and the noise signals are removed by designing a noise separation network. Finally, a subspace back projection network is constructed to reconstruct the initialized denoising image, and the conditional diffusion model is used to refine the initialized denoising results. It is worth noting that the above framework is embedded in the UNet network. Extensive experiments are conducted on simulated and real datasets. The experimental results demonstrate that MSFDN outperforms other state-of-the-art methods and exhibits robustness.https://www.tandfonline.com/doi/10.1080/10095020.2025.2477552Hyperspectral imagedenoisingspatial-spectral-frequencydeep learning |
| spellingShingle | Kai Ren Weiwei Sun Xiangchao Meng Gang Yang MSFDN: multi-scale spatial-spectral-frequency joint denoising network for hyperspectral images Geo-spatial Information Science Hyperspectral image denoising spatial-spectral-frequency deep learning |
| title | MSFDN: multi-scale spatial-spectral-frequency joint denoising network for hyperspectral images |
| title_full | MSFDN: multi-scale spatial-spectral-frequency joint denoising network for hyperspectral images |
| title_fullStr | MSFDN: multi-scale spatial-spectral-frequency joint denoising network for hyperspectral images |
| title_full_unstemmed | MSFDN: multi-scale spatial-spectral-frequency joint denoising network for hyperspectral images |
| title_short | MSFDN: multi-scale spatial-spectral-frequency joint denoising network for hyperspectral images |
| title_sort | msfdn multi scale spatial spectral frequency joint denoising network for hyperspectral images |
| topic | Hyperspectral image denoising spatial-spectral-frequency deep learning |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2025.2477552 |
| work_keys_str_mv | AT kairen msfdnmultiscalespatialspectralfrequencyjointdenoisingnetworkforhyperspectralimages AT weiweisun msfdnmultiscalespatialspectralfrequencyjointdenoisingnetworkforhyperspectralimages AT xiangchaomeng msfdnmultiscalespatialspectralfrequencyjointdenoisingnetworkforhyperspectralimages AT gangyang msfdnmultiscalespatialspectralfrequencyjointdenoisingnetworkforhyperspectralimages |