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- 한국산업응용수학회 JOURNAL OF THE KOREAN SOCIETY FOR INDUSTRIAL AND APPLIED MATHEMATICS Journal of the Korean Society for Industrial and Applied Mathematics Vol.27 No.1
- 발행연도
- 2023.3
- 수록면
- 56 - 74 (19page)
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초록· 키워드
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In order to obtain practical and high-quality satellite images containing highfrequency 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.
목차
ABSTRACT
1. INTRODUCTION
2. RELATED WORKS
3. BACKGROUNDS
4. METHODS
5. EXPERIMENTS
6. CONCLUSION
REFERENCES
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