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Current Optics and Photonics
Vol. 7, No. 2(Apr. 2023)
한국광학회지
Vol. 34, No. 2(Apr. 2023)
K-Light
Vol6, No.2(Apr. 2023)
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Editors Pick
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![[Editor's Pick] Current Optics and Photonics Vol. 7 no. 5 (2023 October)](https://www.osk.or.kr/upload/posts_thumb/2023/10/1698199613_81006700_1751033644.png)
[Editor's Pick] Current Optics and Photonics Vol. 7 no. 5 (2023 October)
Volume-sharing Multi-aperture Imaging (VMAI): A Potential Approach for Volume Reduction for Space-borne Imagers
Jun Ho Lee1,2 *, Seok Gi Han1, Do Hee Kim1, Seokyoung Ju1, Tae Kyung Lee3, Chang Hoon Song3, Myoungjoo Kang3, Seonghui Kim4, and Seohyun Seong4
Current Optics and Photonics Vol. 7 No. 5 (2023 October) pp. 545-556
DOI: https://doi.org/10.3807/COPP.2023.7.5.545
Fig. 2 Illustration of volume-sharing multi-aperture imaging (VMAI) with one wide-field and three narrow-field cameras: (a) Unfolded and (b) compactly folded.
Keywords: Deep-learning, Earth observation, Image fusion, Volume-sharing multi-aperture imaging
OCIS codes: (110.3010) Image reconstruction techniques; (120.3620) Lens system design;(120.4640) Optical instruments; (220.4830) Systems design; (220.4991) Passive remote sensing
Abstract
This paper introduces volume-sharing multi-aperture imaging (VMAI), a potential approach proposed for volume reduction in space-borne imagers, with the aim of achieving high-resolution ground spatial imagery using deep learning methods, with reduced volume compared to conventional approaches. As an intermediate step in the VMAI payload development, we present a phase-1 design targeting a 1-meter ground sampling distance (GSD) at 500 km altitude. Although its optical imaging capability does not surpass conventional approaches, it remains attractive for specific applications on small satellite platforms, particularly surveillance missions. The design integrates one wide-field and three narrowfield cameras with volume sharing and no optical interference. Capturing independent images from the four cameras, the payload emulates a large circular aperture to address diffraction and synthesizes highresolution images using deep learning. Computational simulations validated the VMAI approach, while addressing challenges like lower signal-to-noise (SNR) values resulting from aperture segmentation. Future work will focus on further reducing the reduction ratio and refining SNR management.
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![[하이라이트 논문] 한국광학회지 Vol. 34 No.5 (2023 October)](https://www.osk.or.kr/upload/posts_thumb/2023/10/1698199069_18085100_1960541375-thumb.png)
[하이라이트 논문] 한국광학회지 Vol. 34 No.5 (2023 October)
BL-ASM에서 U-net 기반 위상 홀로그램의 스펙클 노이즈 감소와 이미지 품질 향상
Speckle Noise Reduction and Image Quality Improvement in U-net-based Phase Holograms in BL-ASM
남오승1ㆍ권기철1ㆍ정종래2ㆍ이권연3ㆍ김 남1†
한국광학회지 Vol. 34 No.5 (2023 October) pp. 192-201
DOI: https://doi.org/10.3807/KJOP.2023.34.5.192
Fig. 1 Definition of the coordinate system of sampling window in the angular spectrum method. (a) Coordinate system of sampling window. (b) Coordinate system of source field.
Keywords: 디지털 홀로그래피, 전파 방법, 해상도, 스펙클
OCIS codes: (030.6140) Speckle; (080.1510) Propagation methods; (090.1995) Digital holography; (350.5730) Resolution
초록
Band-limited angular spectrum method (BL-ASM)는 공간주파수 제어의 문제로 aliasing 오류가 발생한다. 본 논문에서는 위상 홀로그 램에 대한 표본화 간격 조정 기법과 딥 러닝 기반의 U-net 모델을 사용한 스펙클 노이즈 감소 및 이미지 품질 향상 기법을 제안하였다. 제안한 기법에서는 넓은 전파 범위에서 aliasing 오류를 제거할 수 있도록 먼저 샘플링 팩터를 계산하여 표본화 간격 조절에 의한 공간주파수를 제어 함으로써 스펙클 노이즈를 감소시킨다. 그 후 딥 러닝 모델을 적용한 위상 홀로그램을 학습시켜 복원 이미지의 품질을 향상시킨다. 다양한 샘플 이미지에 대한 S/W 시뮬레이션에서 기존의 BL-ASM과의 peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM)을 비교할 때 각각 평균 5%, 0.14% 정도 비율이 향상됨을 확인하였다. Abstract
The band-limited angular spectrum method (BL-ASM) causes aliasing errors due to spatial frequency control problems. In this paper, a sampling interval adjustment technique for phase holograms and a technique for reducing speckle noise and improving image quality using a deep-learning-based U-net model are proposed. With the proposed technique, speckle noise is reduced by first calculating the sampling factor and controlling the spatial frequency by adjusting the sampling interval so that aliasing errors can be removed in a wide range of propagation. The next step is to improve the quality of the reconstructed image by learning the phase hologram to which the deep learning model is applied. In the S/W simulation of various sample images, it was confirmed that the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were improved by 5% and 0.14% on average, compared with the existing BL-ASM.