{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:31:28Z","timestamp":1760146288439,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Eight-band one-shot MSFA (multispectral filter array) cameras are innovative technologies used to capture multispectral images by capturing multiple spectral bands simultaneously. They thus make it possible to collect detailed information on the spectral properties of the observed scenes economically. These cameras are widely used for object detection, material analysis, and agronomy. The evolution of one-shot MSFA cameras from 8 to 32 bands makes obtaining much more detailed spectral data possible, which is crucial for applications requiring delicate and precise analysis of the spectral properties of the observed scenes. Our study aims to develop models based on deep learning to estimate the spectral response of this type of camera and provide images close to the spectral properties of objects. First, we prepare our experiment data by projecting them to reflect the characteristics of our camera. Next, we harness the power of deep super-resolution neural networks, such as very deep super-resolution (VDSR), Laplacian pyramid super-resolution networks (LapSRN), and deeply recursive convolutional networks (DRCN), which we adapt to approximate the spectral response. These models learn the complex relationship between 8-band multispectral data from the camera and 31-band multispectral data from the multi-object database, enabling accurate and efficient conversion. Finally, we evaluate the images\u2019 quality using metrics such as loss function, PSNR, and SSIM. The model evaluation revealed that DRCN outperforms others in crucial performance. DRCN achieved the lowest loss with 0.0047 and stood out in image quality metrics, with a PSNR of 25.5059, SSIM of 0.8355, and SAM of 0.13215, indicating better preservation of details and textures. Additionally, DRCN showed the lowest RMSE 0.05849 and MAE 0.0415 values, confirming its ability to minimize reconstruction errors more effectively than VDSR and LapSRN.<\/jats:p>","DOI":"10.3390\/a17110473","type":"journal-article","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T04:10:14Z","timestamp":1729570214000},"page":"473","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning"],"prefix":"10.3390","volume":"17","author":[{"given":"Pierre","family":"Gouton","sequence":"first","affiliation":[{"name":"Laboratoire Imagerie et Vision Artificielle (ImVia), Universit\u00e9 de Bourgogne, 21000 Dijon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1209-5874","authenticated-orcid":false,"given":"Kacoutchy Jean","family":"Ayikpa","sequence":"additional","affiliation":[{"name":"Laboratoire Imagerie et Vision Artificielle (ImVia), Universit\u00e9 de Bourgogne, 21000 Dijon, France"},{"name":"Unit\u00e9 de Recherche et d\u2019Expertise Num\u00e9rique (UREN), Universit\u00e9 Virtuelle de C\u00f4te d\u2019Ivoire, 28 BP 536, Abidjan 28, C\u00f4te d\u2019Ivoire"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diarra","family":"Mamadou","sequence":"additional","affiliation":[{"name":"Laboratoire Imagerie et Vision Artificielle (ImVia), Universit\u00e9 de Bourgogne, 21000 Dijon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,22]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Lanaras, C., Baltsavias, E., and Schindler, K. (2017). Hyperspectral Super-Resolution with Spectral Unmixing Constraints. Remote Sens., 9.","key":"ref_1","DOI":"10.3390\/rs9111196"},{"doi-asserted-by":"crossref","unstructured":"Stuart, M.B., Davies, M., Hobbs, M.J., Pering, T.D., McGonigle, A.J.S., and Willmott, J.R. (2022). High-Resolution Hyperspectral Imaging Using Low-Cost Components: Application within Environmental Monitoring Scenarios. Sensors, 22.","key":"ref_2","DOI":"10.3390\/s22124652"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5147","DOI":"10.1109\/TIP.2020.2980972","article-title":"Boosting Structure Consistency for Multispectral and Multimodal Image Registration","volume":"29","author":"Cao","year":"2020","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"Mohammadi, V., Gouton, P., Ross\u00e9, M., and Katakpe, K.K. (2024). Design and Development of Large-Band Dual-MSFA Sensor Camera for Precision Agriculture. 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