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Deep unfolding blind source unmixing for multicolor fluorescence imaging.

Code ↔ Paper

1 match between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 1 match · it ties a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Network implementation and training procedure ↔ data/three-color brain/mnf.m, the whole file · a weak match · score 0.51 · maximum noise fraction, MNF, matrix

Paper

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The authors' code

MATLAB · 49 lines · 1.5 KB · MIT · 1 match

  1. function [ X,M,SNR ] = mnf( X,k )
  2. % -------------- Maximum Noise Fraction (MNF) Transformation -----------
  3. % Perform MNF transformation on the data and extract features.
  4. % Input:
  5. % X: The data matrix to be transformed, H-by-W-by-B
  6. % k: The number of components to be extracted. If empty, then k = B
  7. % Output:
  8. % X: The matrix after MNF transformation, H-by-W-by-k
  9. % SNR: Signal-to-noise ratio, (B, 1)
  10. [H,W,B] = size(X);
  11. if(~isa(X,'double'))
  12. X = double(X);
  13. end
  14. % Convert to two-dimensional data(p,N),p=B,N=H*W
  15. %X = hyperConvert2d(X);
  16. X = (reshape(X,H*W,B))';
  17. if ~exist('k','var') || isempty(k)
  18. k = B;
  19. end
  20. %% Step 1 Calculate the original covariance matrix sigmaZ and the noise covariance matrix sigmaN
  21. sigmaZ = cov(X');% (p,p)
  22. %X = hyperConvert3d(X,H,W,p);% (H,W,B)
  23. X = reshape(X',H,W,B);
  24. dX = zeros(H-1,W,B);
  25. for i=1:(H-1)
  26. dX(i, :, :) = X(i, :, :) - X(i+1, :, :);
  27. end
  28. % dX = hyperConvert2d(dX);
  29. dX = (reshape(dX,(H-1)*W,B))';
  30. sigmaN = cov(dX');% (p,p)
  31. %% Step 2 Obtain the eigenvectors of the noise covariance matrix and normalize them
  32. [V,D] = eig(sigmaN);% V:(p.p),
  33. [C,I]=sort(diag(D),'descend');
  34. V = V(:,I);D=diag(C);
  35. P = V/sqrt(D);
  36. %% Step 3 Perform standard PCA transformation on the noisy data
  37. sigmaAdj = P'*sigmaZ*P;
  38. [V,D]=eig(sigmaAdj);
  39. [C,I]=sort(diag(D),'descend');
  40. V = V(:,I);D=diag(C);
  41. M = P*V;
  42. M = M(:,1:k)';
  43. SNR = diag(D);% (p,1)
  44. % X = M'*hyperConvert2d(X);% (p,N)
  45. X = M*(reshape(X,H*W,B))';
  46. % X = hyperConvert3d(X, H, W, p);% (H,W,B)=(H,W,p)
  47. X = reshape(X',H,W,k);

mnf.m at commit 8a4a2aa, under MIT · at the source

Overview

Authors: Shiwei Zhu1, Wensong Li1, Yuqi Qin1, Xinshuang Cao1, Yong Deng1
ORCID iDs: Yong Deng
  1. MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
Journal: Biomedical optics express, volume 17, issue 8, pages 4404-4419
Dates: received 15 May 2026; accepted 16 July 2026; published online 27 July 2026
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1364/boe.605821 · PMID 42610145 · PMCID PMC13481066 · OpenAlex W7169615354
Open access: gold, a free copy (OpenAlex)
Status: code verified
Methods: Machine learning
Topic: Random lasers and scattering media (Acoustics and Ultrasonics, Physics and Astronomy), according to OpenAlex
Funding: Brain Science and Brain-like Intelligence Technology - National Science and Technology Major Project (2021ZD0204402)
Citations: cited by 1 paper (Europe PMC); 39 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (none stated) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

ydeng805/DuBsUnmix

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8a4a2aa49b15fc9348515f5fe17f9dd95e3ca0a7, 16 July 2026
Languages: MATLAB (15), Python (2)
Size: 1,125 files, 17 scripts
Software Heritage: not archived
Found in: the references
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (2 files), PyTorch (2 files), SciPy (2 files), OpenCV (1 file), scikit-image (1 file), tifffile (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
19 files

The paper's code and data availability statement is in the Data section.

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 17 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (none stated) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1364/boe.605821.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 1 funder, 36 references.

Cite

This paper

Zhu, S., Li, W., Qin, Y., Cao, X., & Deng, Y. (2026). Deep unfolding blind source unmixing for multicolor fluorescence imaging. Biomedical optics express, 17(8), 4404-4419. https://doi.org/10.1364/boe.605821

BibTeX

@article{zhu2026deep,
author = {Zhu, Shiwei and Li, Wensong and Qin, Yuqi and Cao, Xinshuang and Deng, Yong},
title = {{Deep unfolding blind source unmixing for multicolor fluorescence imaging}},
journal = {Biomedical optics express},
year = {2026},
month = jul,
volume = {17},
number = {8},
pages = {4404--4419},
publisher = {Optica Publishing Group},
issn = {2156-7085},
doi = {10.1364/boe.605821},
url = {https://doi.org/10.1364/boe.605821},
pmid = {42610145},
pmcid = {PMC13481066}
}

RIS

TY - JOUR
AU - Zhu, Shiwei
AU - Li, Wensong
AU - Qin, Yuqi
AU - Cao, Xinshuang
AU - Deng, Yong
TI - Deep unfolding blind source unmixing for multicolor fluorescence imaging
T2 - Biomedical optics express
J2 - Biomed Opt Express
PY - 2026
DA - 2026/07/27
VL - 17
IS - 8
SP - 4404
EP - 4419
SN - 2156-7085
PB - Optica Publishing Group
DO - 10.1364/boe.605821
UR - https://doi.org/10.1364/boe.605821
LA - en
ER -

CSL-JSON

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"title": "Deep unfolding blind source unmixing for multicolor fluorescence imaging",
"container-title": "Biomedical optics express",
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"DOI": "10.1364/boe.605821",
"PMID": "42610145",
"PMCID": "PMC13481066",
"ISSN": "2156-7085",
"publisher": "Optica Publishing Group",
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"issued": {
"date-parts": [
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