Flexible and broadband colloidal quantum dots photodiode array for pixel-level X-ray to near-infrared image fusion
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Published:2023-09-02
Issue:1
Volume:14
Page:
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ISSN:2041-1723
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Container-title:Nature Communications
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language:en
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Short-container-title:Nat Commun
Author:
Liu Jing, Liu Peilin, Shi Tailong, Ke Mo, Xiong Kao, Liu Yuxuan, Chen Long, Zhang Linxiang, Liang Xinyi, Li Hao, Lu Shuaicheng, Lan Xinzheng, Niu GuangdaORCID, Zhang JianbingORCID, Fei PengORCID, Gao LiangORCID, Tang JiangORCID
Abstract
AbstractCombining information from multispectral images into a fused image is informative and beneficial for human or machine perception. Currently, multiple photodetectors with different response bands are used, which require complicated algorithms and systems to solve the pixel and position mismatch problem. An ideal solution would be pixel-level multispectral image fusion, which involves multispectral image using the same photodetector and circumventing the mismatch problem. Here we presented the potential of pixel-level multispectral image fusion utilizing colloidal quantum dots photodiode array, with a broadband response range from X-ray to near infrared and excellent tolerance for bending and X-ray irradiation. The colloidal quantum dots photodiode array showed a specific detectivity exceeding 1012 Jones in visible and near infrared range and a favorable volume sensitivity of approximately 2 × 105 μC Gy−1 cm−3 for X-ray irradiation. To showcase the advantages of pixel-level multispectral image fusion, we imaged a capsule enfolding an iron wire and soft plastic, successfully revealing internal information through an X-ray to near infrared fused image.
Funder
National Natural Science Foundation of China China Postdoctoral Science Foundation
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary
Reference42 articles.
1. Chen, Y. R., Chao, K. L. & Kim, M. S. Machine vision technology for agricultural applications. Comput. Electron. Agr. 36, 173–191 (2002). 2. Livache, C., Martinez, B., Goubet, N., Ramade, J. & Lhuillier, E. Road map for nanocrystal based infrared photodetectors. Front. Chem. 6, 575 (2018). 3. Yadav, S. P. & Yadav, S. Image fusion using hybrid methods in multimodality medical images. Med. Biol. Eng. Comput. 58, 669–687 (2020). 4. Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H. & Aerts, H. Artificial intelligence in radiology. Nat. Rev. Cancer 18, 500–510 (2018). 5. Liu, Y., Liu, S. & Wang, Z. Wang, A general framework for image fusion based on multi-scale transform and sparse representation. Inform. Fusion 24, 147–164 (2015).
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