Journal of the Korean Society for Industrial and Applied Mathematics

Korean Society for Industrial and Applied Mathematics (한국산업응용수학회)
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MULTI-APERTURE IMAGE PROCESSING USING DEEP LEARNING

  • GEONHO HWANG (DEPARTMENT OF MATHEMATICAL SCIENCES, SEOUL NATIONAL UNIVERSITY) ;
  • CHANG HOON SONG (DEPARTMENT OF MATHEMATICAL SCIENCES, SEOUL NATIONAL UNIVERSITY) ;
  • TAE KYUNG LEE (INTERDISCIPLINARY PROGRAM IN ARTIFICIAL INTELLIGENCE, SEOUL NATIONAL UNIVERSITY) ;
  • HOJUN NA (DEPARTMENT OF MATHEMATICAL SCIENCES, SEOUL NATIONAL UNIVERSITY) ;
  • MYUNGJOO KANG (DEPARTMENT OF MATHEMATICAL SCIENCES, SEOUL NATIONAL UNIVERSITY)
  • Received : 2022.12.30
  • Accepted : 2023.03.23
  • Published : 2023.03.25
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Abstract

In order to obtain practical and high-quality satellite images containing high-frequency components, a large aperture optical system is required, which has a limitation in that it greatly increases the payload weight. As an attempt to overcome the problem, many multi-aperture optical systems have been proposed, but in many cases, these optical systems do not include high-frequency components in all directions, and making such an high-quality image is an ill-posed problem. In this paper, we use deep learning to overcome the limitation. A deep learning model receives low-quality images as input, estimates the Point Spread Function, PSF, and combines them to output a single high-quality image. We model images obtained from three rectangular apertures arranged in a regular polygon shape. We also propose the Modulation Transfer Function Loss, MTF Loss, which can capture the high-frequency components of the images. We present qualitative and quantitative results obtained through experiments.

Keywords

Acknowledgement

This work has been supported by the Challengeable Future Defense Technology Research and Development Program through ADD[No. 915020201], the NRF grant[2012R1A2C3010887] and the MSIT/IITP[No. 2021-0-01343].

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