Learned split-spectrum metalens for obstruction-free broadband imaging in the visible
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911402411687936 |
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| author | Yoon, Seungwoo Kang, Dohyun Choi, Eunsue Lee, Sohyun Kim, Seoyeon Choi, Minho Heo, Hyeonsu Shin, Dong-ha Kwak, Suha Majumdar, Arka Rho, Junsuk Baek, Seung-Hwan |
| author_facet | Yoon, Seungwoo Kang, Dohyun Choi, Eunsue Lee, Sohyun Kim, Seoyeon Choi, Minho Heo, Hyeonsu Shin, Dong-ha Kwak, Suha Majumdar, Arka Rho, Junsuk Baek, Seung-Hwan |
| contents | Obstructions such as raindrops, fences, or dust degrade captured images, especially when mechanical cleaning is infeasible. Conventional solutions to obstructions rely on a bulky compound optics array or computational inpainting, which compromise compactness or fidelity. Metalenses composed of subwavelength meta-atoms promise compact imaging, but simultaneous achievement of broadband and obstruction-free imaging remains a challenge, since a metalens that images distant scenes across a broadband spectrum cannot properly defocus near-depth occlusions. Here, we introduce a learned split-spectrum metalens that enables broadband obstruction-free imaging. Our approach divides the spectrum of each RGB channel into pass and stop bands with multi-band spectral filtering and learns the metalens to focus light from far objects through pass bands, while filtering focused near-depth light through stop bands. This optical signal is further enhanced using a neural network. Our learned split-spectrum metalens achieves broadband and obstruction-free imaging with relative PSNR gains of 32.29% and improves object detection and semantic segmentation accuracies with absolute gains of +13.54% mAP, +48.45% IoU, and +20.35% mIoU over a conventional hyperbolic design. This promises robust obstruction-free sensing and vision for space-constrained systems, such as mobile robots, drones, and endoscopes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19403 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learned split-spectrum metalens for obstruction-free broadband imaging in the visible Yoon, Seungwoo Kang, Dohyun Choi, Eunsue Lee, Sohyun Kim, Seoyeon Choi, Minho Heo, Hyeonsu Shin, Dong-ha Kwak, Suha Majumdar, Arka Rho, Junsuk Baek, Seung-Hwan Optics Artificial Intelligence Computer Vision and Pattern Recognition Applied Physics Obstructions such as raindrops, fences, or dust degrade captured images, especially when mechanical cleaning is infeasible. Conventional solutions to obstructions rely on a bulky compound optics array or computational inpainting, which compromise compactness or fidelity. Metalenses composed of subwavelength meta-atoms promise compact imaging, but simultaneous achievement of broadband and obstruction-free imaging remains a challenge, since a metalens that images distant scenes across a broadband spectrum cannot properly defocus near-depth occlusions. Here, we introduce a learned split-spectrum metalens that enables broadband obstruction-free imaging. Our approach divides the spectrum of each RGB channel into pass and stop bands with multi-band spectral filtering and learns the metalens to focus light from far objects through pass bands, while filtering focused near-depth light through stop bands. This optical signal is further enhanced using a neural network. Our learned split-spectrum metalens achieves broadband and obstruction-free imaging with relative PSNR gains of 32.29% and improves object detection and semantic segmentation accuracies with absolute gains of +13.54% mAP, +48.45% IoU, and +20.35% mIoU over a conventional hyperbolic design. This promises robust obstruction-free sensing and vision for space-constrained systems, such as mobile robots, drones, and endoscopes. |
| title | Learned split-spectrum metalens for obstruction-free broadband imaging in the visible |
| topic | Optics Artificial Intelligence Computer Vision and Pattern Recognition Applied Physics |
| url | https://arxiv.org/abs/2601.19403 |