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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12364)

Abstract

We introduce a lensless imaging framework for contemporary computer vision applications in long-wavelength infrared (LWIR). The framework consists of two parts: a novel lensless imaging method that utilizes the idea of local directional focusing for optimal binary sparse coding, and lensless imaging simulator based on Fresnel-Kirchhoff diffraction approximation. Our lensless imaging approach, besides being computationally efficient, is calibration-free and allows for wide FOV imaging. We employ our lensless imaging simulation software for optimizing reconstruction parameters and for synthetic image generation for CNN training. We demonstrate the advantages of our framework on a dual-camera system (RGB-LWIR lensless), where we perform CNN-based human detection using the fused RGB-LWIR data.

Keywords

Lensless imaging Long-wave infrared (LWIR) imaging Diffractive optics Image reconstruction Diffraction simulation Pedestrian detection Human detection Visible-infrared image fusion Faster R-CNNs 

Supplementary material

504475_1_En_15_MOESM1_ESM.pdf (307 kb)
Supplementary material 1 (pdf 306 KB)

Supplementary material 2 (mp4 27273 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Sony CorporationTokyoJapan

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