Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and Destriping
This article proposes a novel regularization method, named <italic>spatio-spectral structure tensor total variation</italic> (<inline-formula><tex-math notation="LaTeX">${\text{S}_{3} \text{TTV}}$</tex-math></inline-formula>), for denoising and destripin...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11072325/ |
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| author | Shingo Takemoto Kazuki Naganuma Shunsuke Ono |
| author_facet | Shingo Takemoto Kazuki Naganuma Shunsuke Ono |
| author_sort | Shingo Takemoto |
| collection | DOAJ |
| description | This article proposes a novel regularization method, named <italic>spatio-spectral structure tensor total variation</italic> (<inline-formula><tex-math notation="LaTeX">${\text{S}_{3} \text{TTV}}$</tex-math></inline-formula>), for denoising and destriping hyperspectral (HS) images. HS images are inevitably contaminated by various types of noise during acquisition process due to the measurement equipment and the environment. For HS image denoising and destriping tasks, spatio-spectral total variation (SSTV) is widely known as a powerful regularization approach that models the spatio-spectral piecewise smoothness. However, since SSTV refers only to the local differences of pixels/bands, edges and textures that extend beyond adjacent pixels are not preserved during denoising process. To address this problem, we newly introduce <inline-formula><tex-math notation="LaTeX">${\text{S}_{3} \text{TTV}}$</tex-math></inline-formula>, which is designed to preserve two essential physical characteristics of HS images: semilocal spatial structures and spectral correlation across all bands. Specifically, we define <inline-formula><tex-math notation="LaTeX">${\text{S}_{3} \text{TTV}}$</tex-math></inline-formula> as the sum of the nuclear norms of spatio-spectral structure tensors, which are matrices formed by arranging second-order spatio-spectral difference vectors within semilocal areas. Furthermore, we formulate the HS image denoising and destriping problem as a constrained convex optimization problem involving <inline-formula><tex-math notation="LaTeX">${\text{S}_{3} \text{TTV}}$</tex-math></inline-formula> and develop an algorithm based on a preconditioned primal-dual splitting method to solve this problem efficiently. Finally, we demonstrate the effectiveness of <inline-formula><tex-math notation="LaTeX">${\text{S}_{3} \text{TTV}}$</tex-math></inline-formula> by comparing it with existing methods, including state-of-the-art ones through denoising and destriping experiments. |
| format | Article |
| id | doaj-art-a4f691ac96ac4ffcb5abbaf421b24f83 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-a4f691ac96ac4ffcb5abbaf421b24f832025-08-20T02:57:45ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118191571917510.1109/JSTARS.2025.358677911072325Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and DestripingShingo Takemoto0https://orcid.org/0000-0002-8075-8983Kazuki Naganuma1https://orcid.org/0000-0002-7180-3017Shunsuke Ono2https://orcid.org/0000-0001-7890-5131Department of Computer Science, Institute of Science Tokyo, Yokohama, JapanInstitute of Engineering of Tokyo University of Agriculture and Technology, Tokyo, JapanDepartment of Computer Science, Institute of Science Tokyo, Yokohama, JapanThis article proposes a novel regularization method, named <italic>spatio-spectral structure tensor total variation</italic> (<inline-formula><tex-math notation="LaTeX">${\text{S}_{3} \text{TTV}}$</tex-math></inline-formula>), for denoising and destriping hyperspectral (HS) images. HS images are inevitably contaminated by various types of noise during acquisition process due to the measurement equipment and the environment. For HS image denoising and destriping tasks, spatio-spectral total variation (SSTV) is widely known as a powerful regularization approach that models the spatio-spectral piecewise smoothness. However, since SSTV refers only to the local differences of pixels/bands, edges and textures that extend beyond adjacent pixels are not preserved during denoising process. To address this problem, we newly introduce <inline-formula><tex-math notation="LaTeX">${\text{S}_{3} \text{TTV}}$</tex-math></inline-formula>, which is designed to preserve two essential physical characteristics of HS images: semilocal spatial structures and spectral correlation across all bands. Specifically, we define <inline-formula><tex-math notation="LaTeX">${\text{S}_{3} \text{TTV}}$</tex-math></inline-formula> as the sum of the nuclear norms of spatio-spectral structure tensors, which are matrices formed by arranging second-order spatio-spectral difference vectors within semilocal areas. Furthermore, we formulate the HS image denoising and destriping problem as a constrained convex optimization problem involving <inline-formula><tex-math notation="LaTeX">${\text{S}_{3} \text{TTV}}$</tex-math></inline-formula> and develop an algorithm based on a preconditioned primal-dual splitting method to solve this problem efficiently. Finally, we demonstrate the effectiveness of <inline-formula><tex-math notation="LaTeX">${\text{S}_{3} \text{TTV}}$</tex-math></inline-formula> by comparing it with existing methods, including state-of-the-art ones through denoising and destriping experiments.https://ieeexplore.ieee.org/document/11072325/Denoisingdestripinghyperspectral imagespatio-spectral regularizationstructure tensortotal variation |
| spellingShingle | Shingo Takemoto Kazuki Naganuma Shunsuke Ono Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and Destriping IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Denoising destriping hyperspectral image spatio-spectral regularization structure tensor total variation |
| title | Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and Destriping |
| title_full | Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and Destriping |
| title_fullStr | Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and Destriping |
| title_full_unstemmed | Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and Destriping |
| title_short | Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and Destriping |
| title_sort | spatio spectral structure tensor total variation for hyperspectral image denoising and destriping |
| topic | Denoising destriping hyperspectral image spatio-spectral regularization structure tensor total variation |
| url | https://ieeexplore.ieee.org/document/11072325/ |
| work_keys_str_mv | AT shingotakemoto spatiospectralstructuretensortotalvariationforhyperspectralimagedenoisinganddestriping AT kazukinaganuma spatiospectralstructuretensortotalvariationforhyperspectralimagedenoisinganddestriping AT shunsukeono spatiospectralstructuretensortotalvariationforhyperspectralimagedenoisinganddestriping |