Evidence map›Paper›PMID 34035213›Full record

ArticleLight, science & applications2021

Deeply learned broadband encoding stochastic hyperspectral imaging.

Wenyi Zhang, Hongya Song, Xin He, Longqian Huang, Xiyue Zhang, Junyan Zheng, Weidong Shen, Xiang Hao, Xu Liu

Abstract readLetter
In one paragraph

Article in Light, science & applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Article
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  10. Advances in Miniaturized Computational Spectrometers.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Review
  11. Article
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  19. Review of Miniaturized Computational Spectrometers.Sensors (Basel, Switzerland) · 2023
    Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Wenyi Zhang *State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Technology, Zhejiang University, Hangzhou, 310027, China.
Hongya Song *State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Technology, Zhejiang University, Hangzhou, 310027, China.
Xin HeState Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Technology, Zhejiang University, Hangzhou, 310027, China.
Longqian HuangState Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Technology, Zhejiang University, Hangzhou, 310027, China.
Xiyue ZhangState Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Technology, Zhejiang University, Hangzhou, 310027, China.
Junyan ZhengState Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Technology, Zhejiang University, Hangzhou, 310027, China.
Weidong ShenState Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Technology, Zhejiang University, Hangzhou, 310027, China.
Xiang HaoState Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Technology, Zhejiang University, Hangzhou, 310027, China. haox@zju.edu.cn.ORCID http://orcid.org/0000-0002-3931-6884
Xu LiuState Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Technology, Zhejiang University, Hangzhou, 310027, China. liuxu@zju.edu.cn.ORCID http://orcid.org/0000-0003-3982-3197

Funding

National Natural Science Foundation of China (National Science Foundation of China) 92050115Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation) LZ21F050003
6 · The paper itself

Abstract

Many applications requiring both spectral and spatial information at high resolution benefit from spectral imaging. Although different technical methods have been developed and commercially available, computational spectral cameras represent a compact, lightweight, and inexpensive solution. However, the tradeoff between spatial and spectral resolutions, dominated by the limited data volume and environmental noise, limits the potential of these cameras. In this study, we developed a deeply learned broadband encoding stochastic hyperspectral camera. In particular, using advanced artificial intelligence in filter design and spectrum reconstruction, we achieved 7000-11,000 times faster signal processing and ~10 times improvement regarding noise tolerance. These improvements enabled us to precisely and dynamically reconstruct the spectra of the entire field of view, previously unreachable with compact computational spectral cameras.

Identifiers

PMID34035213
PMCPMC8149860

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.