Behind the Paper

Catching up with deep learning in spectral imaging

Having a deeper insight into deep learning is beneficial.

Published in Electrical & Electronic Engineering

Go to the profile of Xiang Hao
Xiang Hao
Professor, Zhejiang University
Catching up with deep learning in spectral imaging
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Explore the Research

Nature
Nature Nature

Spectral imaging with deep learning - Light: Science & Applications

This review categorizes deep-learning-based computational spectral imaging methods and provides insight into amplitude, phase, and wavelength-based light encoding strategies for deep-learning spectral reconstruction.

Deep learning continues to show its power for science and applications. Spectral imaging is not an exception. Besides us, there must be many other peers who are also getting involved in the trend of developing spectral imaging with deep learning. Having an overview of these precious contributions is equally beneficial. 

If you are interested in our work, please refer to the review “Spectral imaging with deep learning” published in Light: Science & Applications following the link: https://www.nature.com/articles/s41377-022-00743-6. Alternatively, you may reach me via my email address: haox@zju.edu.cn. 

Go to the profile of Xiang Hao
Xiang Hao
Professor, Zhejiang University

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Electrical and Electronic Engineering
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