Evidence map›Paper›PMID 37598234›Full record

ArticleNature communications2023

Handheld snapshot multi-spectral camera at tens-of-megapixel resolution.

Weihang Zhang, Jinli Suo, Kaiming Dong, Lianglong Li, Xin Yuan, Chengquan Pei, Qionghai Dai

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
7.8field-weighted citation impact, top 2% of its field
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

16 citing papers in PubMed, 49 citations in OpenAlex.

  1. Article
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  5. Miniaturized chaos-assisted Spectrometer.Light, science & applications · 2025
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  11. Advances in Miniaturized Computational Spectrometers.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. 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

7 authors at 4 institutions in 1 country.

Weihang Zhang *Department of Automation, Tsinghua University, Beijing, 100084, China.ORCID http://orcid.org/0000-0003-3717-9473
Jinli Suo *Department of Automation, Tsinghua University, Beijing, 100084, China. jlsuo@tsinghua.edu.cn.ORCID http://orcid.org/0000-0002-3426-1634
Kaiming DongDepartment of Automation, Tsinghua University, Beijing, 100084, China.ORCID http://orcid.org/0000-0002-9748-1482
Lianglong LiDepartment of Automation, Tsinghua University, Beijing, 100084, China.
Xin YuanWestLake University, Hangzhou, 310030, Zhejiang, China.
Chengquan PeiXidian University, Xi'an, 710071, Shaanxi, China.
Qionghai DaiDepartment of Automation, Tsinghua University, Beijing, 100084, China. qhdai@tsinghua.edu.cn.ORCID http://orcid.org/0000-0001-7043-3061
Tsinghua University · CNBeijing Academy of Artificial Intelligence · CNWestlake University · CNXidian University · CN

Funding

Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology) 2020AAA0108202National Natural Science Foundation of China (National Science Foundation of China) 61931012National Natural Science Foundation of China (National Science Foundation of China) 62088102
6 · The paper itself

Abstract

Multi-spectral imaging is a fundamental tool characterizing the constituent energy of scene radiation. However, current multi-spectral video cameras cannot scale up beyond megapixel resolution due to optical constraints and the complexity of the reconstruction algorithms. To circumvent the above issues, we propose a tens-of-megapixel handheld multi-spectral videography approach (THETA), with a proof-of-concept camera achieving 65-megapixel videography of 12 wavebands within visible light range. The high performance is brought by multiple designs: We propose an imaging scheme to fabricate a thin mask for encoding spatio-spectral data using a conventional film camera. Afterwards, a fiber optic plate is introduced for building a compact prototype supporting pixel-wise encoding with a large space-bandwidth product. Finally, a deep-network-based algorithm is adopted for large-scale multi-spectral data decoding, with the coding pattern specially designed to facilitate efficient coarse-to-fine model training. Experimentally, we demonstrate THETA's advantageous and wide applications in outdoor imaging of large macroscopic scenes.

Identifiers

PMID37598234
PMCPMC10439928
OpenAlexW4386001710

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.