Lensless Imaging with Focusing Sparse URA Masks in Long-Wave Infrared and Its Application for Human Detection

Reshetouski, Ilya; Oyaizu, Hideki; Nakamura, Kenichiro; Satoh, Ryuta; Ushiki, Suguru; Tadano, Ryuichi; Ito, Atsushi; Matsubayashi, Jun

doi:10.1007/978-3-030-58529-7_15

Cited by 6 publications

(1 citation statement)

References 34 publications

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“…Lensless cameras have traditionally been developed and used for imaging high-energy radiations such as X-ray and Gamma ray, where fabricating lenses is either extremely expensive or physically unrealizable [8,9,12]. In the last few years, such lensless cameras have been proposed for imaging in visible and infrared wavebands as well; here, the absence of the lens provides advantages in thin form-factor imaging [5,6], better depth-of-field tradeoffs in microscopy [1,2], reduced cost in infrared/multi-spectral imaging [20,24], and provides the ability to do inference from coded measurements [24,29].…”

Section: Introductionmentioning

confidence: 99%

A Simple Framework for 3D Lensless Imaging with Programmable Masks

Zheng¹,

Hua²,

Sankaranarayanan³

et al. 2021

Preprint

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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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