{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T15:15:09Z","timestamp":1788362109958,"version":"build-2803163510"},"reference-count":14,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T00:00:00Z","timestamp":1788307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Conventional image acquisition requires an electrically powered image sensor to be placed directly behind the camera lens, constraining camera placement. To overcome this issue, we introduce M3-RGB as an incoherent-light fiber imaging system in which a multicore, multimode optical fiber passively relays lens images to a remotely located image sensor. Unlike conventional approaches, M3-RGB is designed to operate directly on incoherent light and requires no electrical power or active components at the sensing interface. Because propagation through the fiber yields spatially scrambled patterns, a neural network is used to reconstruct the original scene by exploiting the spatial locality preserved by the multicore structure. In a controlled optical bench setup, where a liquid crystal display monitor displays road-scene images, we construct a paired dataset of scrambled and ground-truth images and quantitatively evaluate reconstruction performance across different fiber core counts, fiber lengths, and calibration settings, utilizing the peak signal-to-noise ratio and structural similarity index measure as performance metrics. By decoupling imaging electronics from the sensing point, this passive remote image relay approach may expand sensor placement options for potential applications such as all-around perception for mobile robots and autonomous vehicles, surveillance, and inspection in confined spaces. Evaluations in real outdoor environments constitute future work.<\/jats:p>","DOI":"10.3390\/s26175582","type":"journal-article","created":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T14:35:29Z","timestamp":1788359729000},"page":"5582","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["M3-RGB: An Imaging Sensor System Using Multicore, Multimode Optical Fiber and Neural Networks"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8544-0135","authenticated-orcid":false,"given":"Seigo","family":"Ito","sequence":"first","affiliation":[{"name":"Toyota Central R&D Labs., Inc., 41-1, Yokomichi, Nagakute 480-1192, Aichi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5061-8760","authenticated-orcid":false,"given":"Isamu","family":"Takai","sequence":"additional","affiliation":[{"name":"Toyota Central R&D Labs., Inc., 41-1, Yokomichi, Nagakute 480-1192, Aichi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]},{"given":"Akari","family":"Kawasaki","sequence":"additional","affiliation":[{"name":"Toyota Central R&D Labs., Inc., 41-1, Yokomichi, Nagakute 480-1192, Aichi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9006-9041","authenticated-orcid":false,"given":"Tadashi","family":"Ichikawa","sequence":"additional","affiliation":[{"name":"Toyota Central R&D Labs., Inc., 41-1, Yokomichi, Nagakute 480-1192, Aichi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2597-118X","authenticated-orcid":false,"given":"Shin","family":"Motooka","sequence":"additional","affiliation":[{"name":"Toyota Central R&D Labs., Inc., 41-1, Yokomichi, Nagakute 480-1192, Aichi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"given":"Minoru","family":"Tanaka","sequence":"additional","affiliation":[{"name":"Toyota Central R&D Labs., Inc., 41-1, Yokomichi, Nagakute 480-1192, Aichi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Heng, L., Choi, B., Cui, Z., Geppert, M., Hu, S., Kuan, B., Liu, P., Nguyen, R., Yeo, Y.C., and Geiger, A. (2019, January 20\u201324). Project AutoVision: Localization and 3D scene perception for an autonomous vehicle with a multi-camera system. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793949"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020, January 13\u201319). nuScenes: A multimodal dataset for autonomous driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.1038\/s41467-019-10057-8","article-title":"Transmission of natural scene images through a multimode fibre","volume":"10","author":"Caramazza","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"187262","DOI":"10.1109\/ACCESS.2025.3626069","article-title":"Streamlined direct image transmission via plastic optical fiber for automotive applications","volume":"13","author":"Fukushima","year":"2025","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., and Caine, B. (2020, January 13\u201319). Scalability in perception for autonomous driving: Waymo open dataset. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"ref_6","unstructured":"NVIDIA (2026, January 26). Physical AI for Autonomous Vehicles. Available online: https:\/\/huggingface.co\/datasets\/nvidia\/PhysicalAI-Autonomous-Vehicles."},{"key":"ref_7","unstructured":"Wilson, B., Qi, W., Agarwal, T., Lambert, J., Singh, J., Khandelwal, S., Pan, B., Kumar, R., Hartnett, A., and Pontes, J.K. (2021, January 6\u201314). Argoverse 2: Next generation datasets for self-driving perception and forecasting. Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks), Online. Available online: https:\/\/arxiv.org\/abs\/2301.00493."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1038\/s41467-022-29178-8","article-title":"All-fiber high-speed image detection enabled by deep learning","volume":"13","author":"Liu","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1038\/s42005-023-01410-x","article-title":"Single-ended recovery of optical fiber transmission matrices using neural networks","volume":"6","author":"Zheng","year":"2023","journal-title":"Commun. Phys."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1038\/s41377-023-01183-6","article-title":"Unsupervised full-color cellular image reconstruction through disordered optical fiber","volume":"12","author":"Hu","year":"2023","journal-title":"Light Sci. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"15880","DOI":"10.1364\/OE.27.015880","article-title":"Fiber bundle imaging resolution enhancement using deep learning","volume":"27","author":"Shao","year":"2019","journal-title":"Opt. Express"},{"key":"ref_12","unstructured":"Teledyne FLIR (2019). Free Teledyne FLIR Thermal Dataset for Algorithm Training (ADAS Dataset), Teledyne FLIR. Available online: https:\/\/oem.flir.com\/en-gb\/solutions\/automotive\/adas-dataset-form\/."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1038\/s41467-026-68638-3","article-title":"Intensity-asymmetric wavefront shaping in nonlocal meta-lens","volume":"17","author":"Yao","year":"2026","journal-title":"Nat. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7882","DOI":"10.1038\/s41467-026-74606-8","article-title":"Varifocal Alvarez metalens array for adaptive light-field imaging","volume":"17","author":"Che","year":"2026","journal-title":"Nat. Commun."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5582\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T14:38:13Z","timestamp":1788359893000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5582"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,2]]},"references-count":14,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175582"],"URL":"https:\/\/doi.org\/10.3390\/s26175582","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,2]]}}}