Mask-Guided Spatial–Spectral MLP Network for High-Resolution Hyperspectral Image Reconstruction
Hyperspectral image (HSI) reconstruction is a critical and indispensable step in spectral compressive imaging (CASSI) systems and directly affects our ability to capture high-quality images in dynamic environments. Recent research has increasingly focused on deep unfolding frameworks for HSI reconst...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/22/7362 |
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| author | Xian-Hua Han Jian Wang Yen-Wei Chen |
| author_facet | Xian-Hua Han Jian Wang Yen-Wei Chen |
| author_sort | Xian-Hua Han |
| collection | DOAJ |
| description | Hyperspectral image (HSI) reconstruction is a critical and indispensable step in spectral compressive imaging (CASSI) systems and directly affects our ability to capture high-quality images in dynamic environments. Recent research has increasingly focused on deep unfolding frameworks for HSI reconstruction, showing notable progress. However, these approaches have to break the optimization task into two sub-problems, solving them iteratively over multiple stages, which leads to large models and high computational overheads. This study presents a simple yet effective method that passes the degradation information (sensing mask) through a deep learning network to disentangle the degradation and the latent target’s representations. Specifically, we design a lightweight MLP block to capture non-local similarities and long-range dependencies across both spatial and spectral domains, and investigate an attention-based mask modelling module to achieve the spatial–spectral-adaptive degradation representationthat is fed to the MLP-based network. To enhance the information flow between MLP blocks, we introduce a multi-level fusion module and apply reconstruction heads to different MLP features for deeper supervision. Additionally, we combine the projection loss from compressive measurements with reconstruction loss to create a dual-domain loss, ensuring consistent optical detection during HS reconstruction. Experiments on benchmark HS datasets show that our method outperforms state-of-the-art approaches in terms of both reconstruction accuracy and efficiency, reducing computational and memory costs. |
| format | Article |
| id | doaj-art-e7de8b46821548099c15f226c7742f5e |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e7de8b46821548099c15f226c7742f5e2025-08-20T01:54:04ZengMDPI AGSensors1424-82202024-11-012422736210.3390/s24227362Mask-Guided Spatial–Spectral MLP Network for High-Resolution Hyperspectral Image ReconstructionXian-Hua Han0Jian Wang1Yen-Wei Chen2Graduate School of Artificial Intelligence and Science, Rikkyo University, Tokyo 171-8501, JapanSchool of Information Science and Engineering, Shandong Normal University, Jinan 250358, ChinaCollege of Information Science and Engineering, Ritsumeikan University, Osaka 603-8577, JapanHyperspectral image (HSI) reconstruction is a critical and indispensable step in spectral compressive imaging (CASSI) systems and directly affects our ability to capture high-quality images in dynamic environments. Recent research has increasingly focused on deep unfolding frameworks for HSI reconstruction, showing notable progress. However, these approaches have to break the optimization task into two sub-problems, solving them iteratively over multiple stages, which leads to large models and high computational overheads. This study presents a simple yet effective method that passes the degradation information (sensing mask) through a deep learning network to disentangle the degradation and the latent target’s representations. Specifically, we design a lightweight MLP block to capture non-local similarities and long-range dependencies across both spatial and spectral domains, and investigate an attention-based mask modelling module to achieve the spatial–spectral-adaptive degradation representationthat is fed to the MLP-based network. To enhance the information flow between MLP blocks, we introduce a multi-level fusion module and apply reconstruction heads to different MLP features for deeper supervision. Additionally, we combine the projection loss from compressive measurements with reconstruction loss to create a dual-domain loss, ensuring consistent optical detection during HS reconstruction. Experiments on benchmark HS datasets show that our method outperforms state-of-the-art approaches in terms of both reconstruction accuracy and efficiency, reducing computational and memory costs.https://www.mdpi.com/1424-8220/24/22/7362hyperspectral image reconstructiondegradationsensing maskspatial–spectral modellinglong dependencyMLP network |
| spellingShingle | Xian-Hua Han Jian Wang Yen-Wei Chen Mask-Guided Spatial–Spectral MLP Network for High-Resolution Hyperspectral Image Reconstruction Sensors hyperspectral image reconstruction degradation sensing mask spatial–spectral modelling long dependency MLP network |
| title | Mask-Guided Spatial–Spectral MLP Network for High-Resolution Hyperspectral Image Reconstruction |
| title_full | Mask-Guided Spatial–Spectral MLP Network for High-Resolution Hyperspectral Image Reconstruction |
| title_fullStr | Mask-Guided Spatial–Spectral MLP Network for High-Resolution Hyperspectral Image Reconstruction |
| title_full_unstemmed | Mask-Guided Spatial–Spectral MLP Network for High-Resolution Hyperspectral Image Reconstruction |
| title_short | Mask-Guided Spatial–Spectral MLP Network for High-Resolution Hyperspectral Image Reconstruction |
| title_sort | mask guided spatial spectral mlp network for high resolution hyperspectral image reconstruction |
| topic | hyperspectral image reconstruction degradation sensing mask spatial–spectral modelling long dependency MLP network |
| url | https://www.mdpi.com/1424-8220/24/22/7362 |
| work_keys_str_mv | AT xianhuahan maskguidedspatialspectralmlpnetworkforhighresolutionhyperspectralimagereconstruction AT jianwang maskguidedspatialspectralmlpnetworkforhighresolutionhyperspectralimagereconstruction AT yenweichen maskguidedspatialspectralmlpnetworkforhighresolutionhyperspectralimagereconstruction |