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用于长距离测量的多视觉系统与一幅高分辨率图像的图像融合。

Image fusion of multi-vision system with one high resolution image for the measurement of long distance.

作者信息

Liu Xingling, Chu Junqiu, Ren Ge, Peng Zhenming

机构信息

Chinese Academy of Sciences, Institute of Optics and Electronics, Chengdu, 610209, China.

University of Electronic Science and Technology of China, Chengdu, 610054, China.

出版信息

Heliyon. 2023 Mar 9;9(3):e14433. doi: 10.1016/j.heliyon.2023.e14433. eCollection 2023 Mar.

DOI:10.1016/j.heliyon.2023.e14433
PMID:36967910
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10031340/
Abstract

The mutual restriction between angle detection accuracy and spatial resolution of image will restrict the detection of long distance by multi-vision system. One higher resolution image of a multi-vision system can be used to improve the resolution of the images of each camera. In present paper, a new fusion method with the help of one higher resolution image is proposed and analytically studied. As an example, an image fusion for the images captured by the combining optical system of camera arrays and the synthetic aperture is studied. Both of the simulation and experiment shows that the proposed method is simple and accuracy.

摘要

角度检测精度与图像空间分辨率之间的相互制约会限制多视觉系统对远距离的检测。多视觉系统的一幅高分辨率图像可用于提高每个相机图像的分辨率。本文提出并深入研究了一种借助一幅高分辨率图像的新型融合方法。作为示例,研究了针对由相机阵列与合成孔径组合光学系统所拍摄图像的图像融合。仿真和实验均表明,所提方法简单且准确。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/677b/10031340/331fcb7e6acd/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/677b/10031340/9c6966a8d4e2/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/677b/10031340/466cce02c5e3/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/677b/10031340/dcf7dfbb623c/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/677b/10031340/b69c1a773c9b/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/677b/10031340/331fcb7e6acd/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/677b/10031340/9c6966a8d4e2/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/677b/10031340/466cce02c5e3/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/677b/10031340/dcf7dfbb623c/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/677b/10031340/b69c1a773c9b/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/677b/10031340/331fcb7e6acd/gr5.jpg

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本文引用的文献

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Binocular vision supports the development of scene segmentation capabilities: Evidence from a deep learning model.双眼视觉支持场景分割能力的发展:来自深度学习模型的证据。
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Image Fusion Techniques: A Survey.图像融合技术:综述
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