{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T07:55:47Z","timestamp":1776239747502,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42176182"],"award-info":[{"award-number":["42176182"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023-YBGY-390"],"award-info":[{"award-number":["2023-YBGY-390"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science Basic Research Foundation of Shaanxi Province","award":["42176182"],"award-info":[{"award-number":["42176182"]}]},{"name":"National Science Basic Research Foundation of Shaanxi Province","award":["2023-YBGY-390"],"award-info":[{"award-number":["2023-YBGY-390"]}]},{"name":"CAS \u201cLight of West China\u201d Program","award":["42176182"],"award-info":[{"award-number":["42176182"]}]},{"name":"CAS \u201cLight of West China\u201d Program","award":["2023-YBGY-390"],"award-info":[{"award-number":["2023-YBGY-390"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Multi-spectral imaging technologies have made great progress in the past few decades. The development of snapshot cameras equipped with a specific multi-spectral filter array (MSFA) allow dynamic scenes to be captured on a miniaturized platform across multiple spectral bands, opening up extensive applications in quantitative and visualized analysis. However, a snapshot camera based on MSFA captures a single band per pixel; thus, the other spectral band components of pixels are all missed. The raw images, which are captured by snapshot multi-spectral imaging systems, require a reconstruction procedure called demosaicing to estimate a fully defined multi-spectral image (MSI). With increasing spectral bands, the challenge of demosaicing becomes more difficult. Furthermore, the existing demosaicing methods will produce adverse artifacts and aliasing because of the adverse effects of spatial interpolation and the inadequacy of the number of layers in the network structure. In this paper, a novel multi-spectral demosaicing method based on a deep convolution neural network (CNN) is proposed for the reconstruction of full-resolution multi-spectral images from raw MSFA-based spectral mosaic images. The CNN is integrated with the channel attention mechanism to protect important channel features. We verify the merits of the proposed method using 5 \u00d7 5 raw mosaic images on synthetic as well as real-world data. The experimental results show that the proposed method outperforms the existing demosaicing methods in terms of spatial details and spectral fidelity.<\/jats:p>","DOI":"10.3390\/s24030943","type":"journal-article","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T09:43:22Z","timestamp":1706780602000},"page":"943","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Snapshot Multi-Spectral Demosaicing Method for Multi-Spectral Filter Array Images Based on Channel Attention Network"],"prefix":"10.3390","volume":"24","author":[{"given":"Xuejun","family":"Zhang","sequence":"first","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yidan","family":"Dai","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Geng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"}]},{"given":"Xuemin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Aerospace Science and Technology, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China"}]},{"given":"Bingliang","family":"Hu","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1111\/1541-4337.12432","article-title":"Principles of hyperspectral microscope imaging techniques and their applications in food quality and safety detection: A review","volume":"18","author":"Pu","year":"2019","journal-title":"Compr. Rev. Food Sci. Food Saf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101400Z","DOI":"10.1117\/12.2253818","article-title":"Tissue classifification of liver pathological tissue specimens image using spectral features","volume":"10140","author":"Hashimoto","year":"2017","journal-title":"Proc. SPIE"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.compag.2014.08.010","article-title":"Comparison of the efficacy of spectral pre-treatments for wheat and weed discrimination in outdoor conditions","volume":"108","author":"Hadoux","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TGRS.2018.2856370","article-title":"Tracking in aerial hyperspectral videos using deep kernelized correlation filters","volume":"57","author":"Uzkent","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3719","DOI":"10.1109\/TIP.2020.2965302","article-title":"Material based object tracking in hyperspectral videos","volume":"29","author":"Xiong","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1007\/s11263-008-0176-y","article-title":"Multi-spectral imaging by optimized wide band illumination","volume":"86","author":"Chi","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"85","DOI":"10.2352\/J.ImagingSci.Technol.2004.48.2.art00003","article-title":"Six-band HDTV camera system for spectrum-based color reproduction","volume":"48","author":"Ohsawa","year":"2004","journal-title":"J. Imaging Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"21626","DOI":"10.3390\/s141121626","article-title":"Multispectral filter arrays: Recent advances and practical implementation","volume":"14","author":"Lapray","year":"2014","journal-title":"Sensors"},{"key":"ref_9","first-page":"80","article-title":"A compact snapshot multispectral imager with a monolithically integrated per-pixel filter mosaic","volume":"8974","author":"Geelen","year":"2014","journal-title":"Proc. SPIE"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"C14","DOI":"10.1364\/AO.53.000C14","article-title":"Combining transverse field detectors and color filter arrays to improve multispectral imaging systems","volume":"53","author":"Valero","year":"2014","journal-title":"Appl. Opt."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3048","DOI":"10.1109\/TIP.2015.2436342","article-title":"A practical one-shot multispectral imaging system using a single image sensor","volume":"24","author":"Monno","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Thomas, J.B., Lapray, P.J., Gouton, P., and Clerc, C. (2016). Spectral characterization of a prototype SFA camera for joint visible and NIR acquisition. Sensors, 16.","DOI":"10.3390\/s16070993"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7173","DOI":"10.1364\/OE.20.007173","article-title":"Spectral Hybrid-resolution multispectral imaging using color filter array","volume":"20","author":"Murakami","year":"2012","journal-title":"Opt. Express"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1109\/TIP.2016.2634120","article-title":"Adaptive multispectral demosaicing based on frequency domain analysis of spectral correlation","volume":"26","author":"Jaiswal","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fukuda, H., Uchiyama, T., Haneishi, H., Yamaguchi, M., and Ohyama, N. (2005, January 17\u201320). Development of a 16-band multispectral image archiving system. Proceedings of the SPIE Electronic Imaging: Color Imaging X, San Jose, CA, USA.","DOI":"10.1117\/12.592533"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1298","DOI":"10.1364\/OPTICA.397214","article-title":"Spectral DiffuserCam: Lensless snapshot hyperspectral imaging with a spectral filter array","volume":"7","author":"Monakhova","year":"2020","journal-title":"Optica"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MSP.2005.1407714","article-title":"Demosaicing: Color filter array interpolation","volume":"22","author":"Gunturk","year":"2005","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_18","first-page":"204","article-title":"Multispectral demosaicing using guided filter","volume":"8299","author":"Monno","year":"2012","journal-title":"Digit. Photogr. VIII"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3336471","article-title":"PPI Edge Infused Spatial\u2013Spectral Adaptive Residual Network for Multispectral Filter Array Image Demosaicing","volume":"61","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"116893","DOI":"10.1016\/j.image.2022.116893","article-title":"Generic multispectral demosaicking using spectral correlation between spectral bands and pseudo-panchromatic image","volume":"110","author":"Rathi","year":"2023","journal-title":"Signal Process. Image Commun."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/TCI.2022.3140554","article-title":"Adaptive and Progressive Multispectral Image Demosaicking","volume":"8","author":"Gupta","year":"2022","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_22","unstructured":"Brauers, J., and Aach, T. (2006, January 5\u20136). A Color Filter Array based Multispectral Camera. Proceedings of the Workshop Farbbildverarbeitung, Ilmenau, Germany."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, X., Thomas, J.B., Hardeberg, J.Y., and Gouton, P. (2013, January 5\u20136). Discrete wavelet transform based multispectral filter array demosaicing. Proceedings of the 2013 Colour and Visual Computing Symposium (CVCS), Gjovik, Norway.","DOI":"10.1109\/CVCS.2013.6626274"},{"key":"ref_24","unstructured":"Driesen, J., and Scheunders, P. (2004, January 24\u201327). Wavelet-based color filter array demosaicing. Proceedings of the International Conference on Image Processing (ICIP), Singapore."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3550","DOI":"10.1109\/TIP.2006.877476","article-title":"Binary tree-based generic demosaicing algorithm for multispectral filter arrays","volume":"15","author":"Miao","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TCI.2017.2691553","article-title":"Multispectral demosaicing using pseudo-panchromatic image","volume":"3","author":"Mihoubi","year":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_27","unstructured":"Xu, L., Ren, J.S., Liu, C., and Jia, J. (2014, January 8\u201313). Deep convolutional neural network for image deconvolution. Proceedings of the International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., and Tang, X. (2014, January 6\u201312). Learning a deep convolutional network for image super-resolution. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (2016, January 27\u201330). Deeply-recursive convolutional network for image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.181"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1109\/TCSVT.2021.3078559","article-title":"Hyperspectral image super resolution via deep prior regularization with parameter estimation","volume":"32","author":"Wang","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TII.2023.3256400","article-title":"Cross-modal Fusion Convolutional Neural Networks with Online Soft Label Training Strategy for Mechanical Fault Diagnosis","volume":"20","author":"Xu","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2023.02.012","article-title":"CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery","volume":"95","author":"Xu","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_33","unstructured":"Xie, J., Xu, L., and Chen, E. (2012, January 3\u20136). Image denoising and inpainting with deep neural networks. Proceedings of the International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4556","DOI":"10.1109\/TIP.2015.2468172","article-title":"Dictionary pair learning on Grassmann manifolds for image denoising","volume":"24","author":"Zeng","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Pan, Z., Li, B., Cheng, H., and Bao, Y. (2020, January 4\u20138). Deep residual network for MSFA raw image denoising. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053201"},{"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 19\u201320). 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","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_38","doi-asserted-by":"crossref","unstructured":"Li, J., Wu, C., Song, R., Li, Y., and Liu, F. (2020, January 14\u201319). Adaptive weighted attention network with camera spectral sensitivity prior for spectral reconstruction from RGB images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00239"},{"key":"ref_39","unstructured":"Shinoda, K., Yoshiba, S., and Hasegawa, M. (2018). Deep demosaicing for multispectral filter arrays. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wisotzky, E.L., Daudkane, C., Hilsmann, A., and Eisert, P. (2022, January 27\u201330). Hyperspectral Demosaicing of Snapshot Camera Images Using Deep Learning. Proceedings of the DAGM German Conference on Pattern Recognition, Konstanz, Germany.","DOI":"10.1007\/978-3-031-16788-1_13"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_43","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classifification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems 25 (NIPS 2012), Lake Tahoe, NV, USA."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1109\/JSEN.2018.2876774","article-title":"Single-sensor RGB-NIR imaging: High-quality system design and prototype implementation","volume":"19","author":"Monno","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"8423","DOI":"10.1007\/s11042-020-10035-z","article-title":"PSNR vs. SSIM: Imperceptibility quality assessment for image steganography","volume":"80","author":"Setiadi","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"103631","DOI":"10.1016\/j.infrared.2021.103631","article-title":"Hyperspectral image super-resolution via subspace-based fast low tensor multi-rank regularization","volume":"116","author":"Long","year":"2021","journal-title":"Infrared Phys. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/943\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:52:47Z","timestamp":1760104367000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/943"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,1]]},"references-count":46,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24030943"],"URL":"https:\/\/doi.org\/10.3390\/s24030943","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,1]]}}}