Evidence map›Paper›PMID 38272927›Full record

ArticleNature communications2024

Noise learning of instruments for high-contrast, high-resolution and fast hyperspectral microscopy and nanoscopy.

Hao He, Maofeng Cao, Yun Gao, Peng Zheng, Sen Yan, Jin-Hui Zhong, Lei Wang, Dayong Jin, Bin Ren

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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.

Hao HePen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China.ORCID http://orcid.org/0000-0002-9825-2681
Maofeng CaoState Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
Yun GaoPen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China.
Peng ZhengPen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China.
Sen YanState Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
Jin-Hui ZhongDepartment of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China. zhongjh@sustech.edu.cn.ORCID http://orcid.org/0000-0002-2134-3235
Lei WangPen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China. wanglei33@xmu.edu.cn.ORCID http://orcid.org/0000-0001-7276-0832
Dayong JinDepartment of Biomedical Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.ORCID http://orcid.org/0000-0003-1046-2666
Bin RenState Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China. bren@xmu.edu.cn.ORCID http://orcid.org/0000-0002-9821-5864

Funding

China Postdoctoral Science Foundation 2022M711482National Natural Science Foundation of China (National Science Foundation of China) 22021001, 22227802National Natural Science Foundation of China (National Science Foundation of China) 22174120National Natural Science Foundation of China (National Science Foundation of China) 22204069National Natural Science Foundation of China (National Science Foundation of China) 22272072Shenzhen Science and Technology Innovation Commission RCBS20221008093127073, JCYJ20230807093405012
6 · The paper itself

Abstract

The low scattering efficiency of Raman scattering makes it challenging to simultaneously achieve good signal-to-noise ratio (SNR), high imaging speed, and adequate spatial and spectral resolutions. Here, we report a noise learning (NL) approach that estimates the intrinsic noise distribution of each instrument by statistically learning the noise in the pixel-spatial frequency domain. The estimated noise is then removed from the noisy spectra. This enhances the SNR by ca. 10 folds, and suppresses the mean-square error by almost 150 folds. NL allows us to improve the positioning accuracy and spatial resolution and largely eliminates the impact of thermal drift on tip-enhanced Raman spectroscopic nanoimaging. NL is also applicable to enhance SNR in fluorescence and photoluminescence imaging. Our method manages the ground truth spectra and the instrumental noise simultaneously within the training dataset, which bypasses the tedious labelling of huge dataset required in conventional deep learning, potentially shifting deep learning from sample-dependent to instrument-dependent.

Identifiers

PMID38272927
PMCPMC10810791

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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.