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摘要
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Single-shot three-dimensional (3D) imaging with compact device footprint, high imaging quality, and fast processing speed is challenging in computational imaging. Mask-based lensless imagers, which replace the bulky optics with customized thin optical masks, are portable and lightweight, and can recover 3D object from a snap-shot image. Existing lensless imaging typically requires extensive calibration of its point spread function and heavy computational resources to reconstruct the object. Here we overcome these challenges and demonstrate a compact and learnable lensless 3D camera for real-time photorealistic imaging. We custom designed and fabricated the optical phase mask with an optimized spatial frequency support and axial resolving ability. We developed a simple and robust physics-aware deep learning model with adversarial learning module for real-time depth-resolved photorealistic reconstructions. Our lensless imager does not require calibrating the point spread function and has the capability to resolve depth and see-through opaque obstacles to image features being blocked, enabling broad applications in computational imaging.(c) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
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