{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T18:30:27Z","timestamp":1779906627331,"version":"3.53.1"},"reference-count":54,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"High-level Talents Programme of National University of Defense Technology"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral Imaging (HSI) excels in material identification and capturing spectral details and is widely utilized in various fields, including remote sensing and environmental monitoring. However, in real-world applications, HSI is often affected by Stray Light Interference (SLI), which severely degrades both its spatial and spectral quality, thereby reducing overall image accuracy and usability. Existing hardware solutions are often expensive and add complexity to the system, and despite these efforts, they cannot fully eliminate SLI. Traditional algorithmic methods, on the other hand, struggle to capture the intricate spatial\u2013spectral dependencies needed for effective restoration, particularly in complex noise scenarios. Deep learning methods present a promising alternative because of their flexibility in handling complex data and strong restoration capabilities. To tackle this challenge, we propose MambaHR, a novel State Space Model (SSM) for HSI restoration under SLI. MambaHR incorporates state space modules and channel attention mechanisms, effectively capturing and integrating global and local spatial\u2013spectral dependencies while preserving critical spectral details. Additionally, we constructed a synthetic hyperspectral dataset with SLI by simulating light spots of varying intensities and shapes across spectral channels, thereby realistically replicating the interference observed in real-world conditions. Experimental results demonstrate that MambaHR significantly outperforms existing methods across multiple benchmark HSI datasets, exhibiting superior performance in preserving spectral accuracy and enhancing spatial resolution. This method holds great potential for improving HSI processing applications in fields such as remote sensing and environmental monitoring.<\/jats:p>","DOI":"10.3390\/rs16244661","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T03:51:08Z","timestamp":1734061868000},"page":"4661","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MambaHR: State Space Model for Hyperspectral Image Restoration Under Stray Light Interference"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4091-8399","authenticated-orcid":false,"given":"Zhongyang","family":"Xing","sequence":"first","affiliation":[{"name":"College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China"},{"name":"State Key Laboratory of Pulsed Power Laser Technology, Changsha 410073, China"},{"name":"Hunan Provincial Key Laboratory of High Energy Laser Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8194-1735","authenticated-orcid":false,"given":"Haoqian","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China"},{"name":"State Key Laboratory of Pulsed Power Laser Technology, Changsha 410073, China"},{"name":"Hunan Provincial Key Laboratory of High Energy Laser Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ju","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China"},{"name":"State Key Laboratory of Pulsed Power Laser Technology, Changsha 410073, China"},{"name":"Hunan Provincial Key Laboratory of High Energy Laser Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangai","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China"},{"name":"State Key Laboratory of Pulsed Power Laser Technology, Changsha 410073, China"},{"name":"Hunan Provincial Key Laboratory of High Energy Laser Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6307-8425","authenticated-orcid":false,"given":"Zhongjie","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China"},{"name":"State Key Laboratory of Pulsed Power Laser Technology, Changsha 410073, China"},{"name":"Hunan Provincial Key Laboratory of High Energy Laser Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.54097\/ajst.v6i2.9435","article-title":"Review of Hyperspectral Imaging in Environmental Monitoring Progress and Applications","volume":"6","author":"Zhang","year":"2023","journal-title":"Acad. J. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rajabi, R., Zehtabian, A., Singh, K.D., Tabatabaeenejad, A., Ghamisi, P., and Homayouni, S. (2024). Hyperspectral Imaging in environmental monitoring and analysis. Front. Environ. Sci., 11.","DOI":"10.3389\/fenvs.2023.1353447"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/MGRS.2019.2902525","article-title":"Hypersectral Imaging for Military and Security Applications: Combining Myriad Processing and Sensing Techniques","volume":"7","author":"Shimoni","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1109\/JPROC.2009.2013561","article-title":"Automated hyperspectral cueing for civilian search and rescue","volume":"97","author":"Eismann","year":"2009","journal-title":"Proc. IEEE"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e33208","DOI":"10.1016\/j.heliyon.2024.e33208","article-title":"Hyperspectral Imaging and Its Applications: A Review","volume":"10","author":"Bhargava","year":"2024","journal-title":"Heliyon"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.infrared.2018.06.011","article-title":"A method of reducing stray light of 1.5 \u03bcm laser 3D vision system","volume":"92","author":"Qu","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_7","unstructured":"Shen, S., Zhu, J., Huang, X., and Shen, W. (2018, January 5\u20137). Suppression of the Self-Radiation Stray Light of Long-Wave Thermal Infrared Imaging Spectrometers. Proceedings of the 5th International Symposium of Space Optical Instruments and Applications, Beijing, China."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6112","DOI":"10.1364\/AO.531177","article-title":"Stray light analysis and suppression of a UV multiple sub-pupil ultra-spectral imager","volume":"63","author":"Zhang","year":"2024","journal-title":"Appl. Opt."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"33938","DOI":"10.1109\/ACCESS.2024.3369471","article-title":"Study on stray light testing and suppression techniques for large-field of view multispectral space optical systems","volume":"12","author":"Lu","year":"2024","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"822","DOI":"10.37188\/CO.2019-0036","article-title":"Optical system design and stray light suppression of catadioptric space camera","volume":"13","author":"Feng","year":"2020","journal-title":"Chin. Opt."},{"key":"ref_11","first-page":"39","article-title":"Laser Active Jamming of Photo-electric Imaging System and Its Computer Simulation","volume":"43","author":"Li","year":"2006","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_12","first-page":"011010","article-title":"Simulation study of strong light interference effect in temporally and spatially modulated Fourier transform imaging spectrometer","volume":"34","author":"Meng","year":"2022","journal-title":"High Power Laser Part. Beams"},{"key":"ref_13","first-page":"989","article-title":"Impact of laser jamming on target detection performance in CCD imaging system","volume":"41","author":"Xu","year":"2012","journal-title":"Infrared Laser Eng."},{"key":"ref_14","first-page":"313","article-title":"Through-field Investigation of Stray Light for the Fore-optics of an Airborne Hyperspectral Imager","volume":"6","author":"Cha","year":"2022","journal-title":"Curr. Opt. Photonics"},{"key":"ref_15","first-page":"0751406","article-title":"Development and Prospect of Stray Light Suppression and Evaluation Technology (Invited)","volume":"51","author":"Wang","year":"2022","journal-title":"Acta Photonica Sin."},{"key":"ref_16","first-page":"28","article-title":"Smart filters: Protect from laser threats","volume":"Volume 9081","author":"Donval","year":"2014","journal-title":"Proceedings of the Laser Technology for Defense and Security X"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1080\/10803548.2015.1083674","article-title":"Analysis of the selected optical parameters of filters protecting against hazardous infrared radiation","volume":"22","author":"Gralewicz","year":"2016","journal-title":"Int. J. Occup. Saf. Ergon."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Matsniev, I., Andriichuk, V., Chumak, O., Derzhypolsky, A., Derzhypolska, L., Khodakovskiy, V., Perederiy, O., and Negriyko, A. (2022, January 4\u20138). The Threshold of Laser-Induced Damage of Image Sensors in Open Atmosphere. Proceedings of the International Conference on Nanotechnology and Nanomaterials, Palma, Spain.","DOI":"10.1007\/978-3-031-42708-4_20"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3502","DOI":"10.1109\/TIP.2012.2192126","article-title":"Modeling the performance of image restoration from motion blur","volume":"21","author":"Boracchi","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/MSP.2017.2717489","article-title":"Image restoration: From sparse and low-rank priors to deep priors [lecture notes]","volume":"34","author":"Zhang","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.neucom.2021.03.035","article-title":"A survey: Deep learning for Hyperspectral Image classification with few labeled samples","volume":"448","author":"Jia","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1668","DOI":"10.1109\/JAS.2023.123681","article-title":"Hyperspectral Image super-resolution meets deep learning: A survey and perspective","volume":"10","author":"Wang","year":"2023","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1109\/JSTARS.2021.3133021","article-title":"Hyperspectral Image classification\u2014Traditional to deep models: A survey for future prospects","volume":"15","author":"Ahmad","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","unstructured":"Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is All you Need. Proceedings of the Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_25","unstructured":"Gu, A., Goel, K., and R\u00e9, C. (2022, January 25\u201329). Efficiently Modeling Long Sequences with Structured State Spaces. Proceedings of the International Conference on Learning Representations (ICLR), Virtual."},{"key":"ref_26","unstructured":"Gu, A., and Dao, T. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1109\/LGRS.2017.2764059","article-title":"Automatic Hyperspectral Image restoration using sparse and low-rank modeling","volume":"14","author":"Rasti","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"140302","DOI":"10.1007\/s11432-022-3609-4","article-title":"A survey on Hyperspectral Image restoration: From the view of low-rank tensor approximation","volume":"66","author":"Liu","year":"2023","journal-title":"Sci. China Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhao, B., Ulfarsson, M.O., and Sigurdsson, J. (2023, January 16\u201321). Hyperspectral Image Denoising Using Low-Rank and Sparse Model Based Deep Unrolling. Proceedings of the IGARSS 2023\u20142023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA.","DOI":"10.1109\/IGARSS52108.2023.10282195"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, M., Liu, J., Fu, Y., Zhang, Y., and Dou, D. (2023, January 18\u201322). Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00562"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2516","DOI":"10.1109\/TGRS.2019.2952062","article-title":"A single model CNN for Hyperspectral Image denoising","volume":"58","author":"Maffei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sidorov, O., and Hardeberg, J.Y. (2019, January 27\u201328). Deep Hyperspectral Prior: Single-Image Denoising, Inpainting, Super-Resolution. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00477"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shi, Z., Chen, C., Xiong, Z., Liu, D., and Wu, F. (2018, January 18\u201322). Hscnn+: Advanced cnn-based hyperspectral recovery from rgb images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00139"},{"key":"ref_34","first-page":"3315970","article-title":"MSDformer: Multiscale Deformable Transformer for Hyperspectral Image Super-Resolution","volume":"61","author":"Chen","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","first-page":"3458174","article-title":"Three-Dimension spatial\u2013spectral Attention Transformer for Hyperspectral Image Denoising","volume":"62","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cai, Y., Lin, J., Lin, Z., Wang, H., Zhang, Y., Pfister, H., Timofte, R., and Van Gool, L. (2022, January 18\u201324). Mst++: Multi-stage spectral-wise transformer for efficient spectral reconstruction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00090"},{"key":"ref_37","unstructured":"Qu, H., Ning, L., An, R., Fan, W., Derr, T., Liu, H., Xu, X., and Li, Q. (2024). A survey of mamba. arXiv."},{"key":"ref_38","first-page":"1","article-title":"Ssumamba: Spatial\u2013spectral selective state space model for Hyperspectral Image denoising","volume":"62","author":"Fu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_39","unstructured":"Dong, J., Yin, H., Li, H., Li, W., Zhang, Y., Khan, S., and Khan, F.S. (2024). Dual Hyperspectral Mamba for Efficient Spectral Compressive Imaging. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"27521","DOI":"10.1038\/s41598-024-78472-6","article-title":"A model for suppressing stray light in astronomical images based on deep learning","volume":"14","author":"Chen","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Xing, Y., Huang, Y., Chang, J., Wu, Z., Duan, Z., and Song, J. (Optica Open, 2023). Stray light suppression of opto-mechanical system based on deep reinforcement learning, Optica Open.","DOI":"10.1364\/opticaopen.24155175"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, Y., Niu, Z., Sun, Q., Xiao, H., and Li, H. (2022). BSC-Net: Background Suppression Algorithm for Stray Lights in Star Images. Remote Sens., 14.","DOI":"10.3390\/rs14194852"},{"key":"ref_43","first-page":"230210","article-title":"Reinforcement learning-based stray light suppression study for space-based gravitational wave detection telescope system","volume":"51","author":"Ziyang","year":"2024","journal-title":"Opto-Electron. Eng."},{"key":"ref_44","unstructured":"Yokoya, N., and Iwasaki, A. (2016). Airborne hyperspectral Data over Chikusei, Space Application Laboratory, University of Tokyo. Technical Report SAL-2016-05-27."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1109\/JSTARS.2019.2911113","article-title":"Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use and Land Cover Classification: Outcome of the 2018 IEEE GRSS Data Fusion Contest","volume":"12","author":"Xu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3205","DOI":"10.1080\/01431160802559046","article-title":"A comparative study of spatial approaches for urban mapping using hyperspectral ROSIS images over Pavia City, northern Italy","volume":"30","author":"Huang","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/TCI.2020.2996075","article-title":"Learning spatial\u2013spectral Prior for Super-Resolution of Hyperspectral Imagery","volume":"6","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_48","first-page":"1","article-title":"Hyperspectral Image Super-Resolution via Recurrent Feedback Embedding and spatial\u2013spectral Consistency Regularization","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_50","unstructured":"Yuhas, R.H., Goetz, A.F., and Boardman, J.W. (1992, January 1\u20135). Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm. Proceedings of the Jet Propulsion Laboratory (JPL), Summaries of the Third Annual JPL Airborne Geoscience Workshop, Pasadena, CA, USA."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/MGRS.2015.2440094","article-title":"Hyperspectral pansharpening: A review","volume":"3","author":"Loncan","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_52","unstructured":"Wald, L. (2002). Data Fusion: Definitions and Architectures: Fusion of Images of Different Spatial Resolutions, Presses des MINES."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, X., Zhou, J., Qiao, Y., and Dong, C. (2023, January 18\u201322). Activating More Pixels in Image Super-Resolution Transformer. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02142"},{"key":"ref_54","unstructured":"Guo, H., Li, J., Dai, T., Ouyang, Z., Ren, X., and Xia, S.T. (October, January 29). MambaIR: A Simple Baseline for Image Restoration with State-Space Model. Proceedings of the European Conference on Computer Vision (ECCV), Milan, Italy."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4661\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:54:09Z","timestamp":1760115249000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4661"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,13]]},"references-count":54,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16244661"],"URL":"https:\/\/doi.org\/10.3390\/rs16244661","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,13]]}}}