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Turn a silicon camera into an ingaas camera

  • Feifan Lv
  • , Yinqiang Zheng
  • , Bohan Zhang
  • , Feng Lu*
  • *Corresponding author for this work
  • Beihang University
  • National Institute of Informatics
  • Peng Cheng Laboratory

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Short-wave infrared (SWIR) imaging has a wide range of applications for both industry and civilian. However, the InGaAs sensors commonly used for SWIR imaging suffer from a variety of drawbacks, including high price, low resolution, unstable quality, and so on. In this paper, we propose a novel solution for SWIR imaging using a common Silicon sensor, which has cheaper price, higher resolution and better technical maturity compared with the specialized InGaAs sensor. Our key idea is to approximate the response of the InGaAs sensor by exploiting the largely ignored sensitivity of a Silicon sensor, weak as it is, in the SWIR range. To this end, we build a multi-channel optical system to collect a new SWIR dataset and present a physically meaningful three-stage image processing algorithm on the basis of CNN. Both qualitative and quantitative experiments show promising experimental results, which demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
PublisherIEEE Computer Society
Pages5980-5988
Number of pages9
ISBN (Electronic)9781728132938
DOIs
StatePublished - Jun 2019
Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States
Duration: 16 Jun 201920 Jun 2019

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2019-June
ISSN (Print)1063-6919

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
Country/TerritoryUnited States
CityLong Beach
Period16/06/1920/06/19

Keywords

  • Image and Video Synthesis
  • Low-level Vision

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