A Simple Framework for 3D Lensless Imaging with Programmable Masks

• URL: http://arxiv.org/abs/2108.07966v1
• Date: Wed, 18 Aug 2021 04:05:33 GMT
• Title: A Simple Framework for 3D Lensless Imaging with Programmable Masks
• Authors: Yucheng Zheng, Yi Hua, Aswin C. Sankaranarayanan, M. Salman Asif
• Abstract summary: We propose a lensless imaging system that captures a small number of measurements using different patterns on a programmable mask. First, we present a fast recovery algorithm to recover textures on a fixed number of depth planes in the scene. Second, we consider the mask design problem, for programmable lensless cameras, and provide a design template for optimizing the mask patterns. Third, we use a refinement network as a post-processing step to identify and remove artifacts in the reconstruction.
• Score: 37.35255907261072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lensless cameras provide a framework to build thin imaging systems by replacing the lens in a conventional camera with an amplitude or phase mask near the sensor. Existing methods for lensless imaging can recover the depth and intensity of the scene, but they require solving computationally-expensive inverse problems. Furthermore, existing methods struggle to recover dense scenes with large depth variations. In this paper, we propose a lensless imaging system that captures a small number of measurements using different patterns on a programmable mask. In this context, we make three contributions. First, we present a fast recovery algorithm to recover textures on a fixed number of depth planes in the scene. Second, we consider the mask design problem, for programmable lensless cameras, and provide a design template for optimizing the mask patterns with the goal of improving depth estimation. Third, we use a refinement network as a post-processing step to identify and remove artifacts in the reconstruction. These modifications are evaluated extensively with experimental results on a lensless camera prototype to showcase the performance benefits of the optimized masks and recovery algorithms over the state of the art.

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