De-occluding broadband metalens

Obstructions such as raindrops, fences, or dust degrade images, especially when cleaning is infeasible. Conventional approaches use bulky compound-lens arrays or computational inpainting, compromising compactness or fidelity. Metalenses promise compact imaging, but achieving both broadband and obstruction-free imaging remains challenging, since a metalens cannot simultaneously focus distant scenes and defocus nearby occlusions across a broadband spectrum. We introduce a de-occluding broadband metalens that suppresses obstructions while enabling broadband imaging with a learned phase profile. Our approach divides the spectrum of each color channel into pass and stop bands with multi-band spectral filtering; the metalens focuses light from far objects through pass bands, then light from nearby obstructions falls in the stop bands and is rejected. A neural network further enhances imaging quality. Compared with a conventional hyperbolic metalens under obstructions, our system improves PSNR by 32.29% and raises performance on object detection and two semantic segmentation benchmarks by 13.54%, 48.45% and 20.35%, respectively. This promises robust obstruction-free sensing and vision for compact systems, including mobile robots, drones, and endoscopes. The authors demonstrate a de-occluding metalens that separates spectral bands to suppress nearby obstructions while preserving broadband imaging. The approach improves image quality and machine vision performance for robust sensing and vision.