Machine Learning in Photonics: Progress, Challenges, and Future Prospects

A special issue of Photonics (ISSN 2304-6732). This special issue belongs to the section "Optical Interaction Science".

Deadline for manuscript submissions: closed (31 August 2025) | Viewed by 1232

Special Issue Editors


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Guest Editor
Shenzhen International Graduate School, Tsinghua University, University Town of Shenzhen, Nanshan District, Shenzhen 518055, China
Interests: machine learning; polarimetry; Mueller matrix; biophotonics

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Guest Editor
Shenzhen International Graduate School, Tsinghua University, University Town of Shenzhen, Nanshan District, Shenzhen 518055, China
Interests: polarization imaging; polarization-based digital pathology; machine learning; optical imaging; biophotonics

Special Issue Information

Dear Colleagues,

In recent decades, machine learning has brought transformative advances across various scientific domains, and especially to Photonics and Optics. In these disciplines, where light acts as an information carrier, the introduction of machine learning methodologies has emerged as a cornerstone that fundamentally enhances our capabilities for feature detection and characterization, elevating the resolution, speed, and quality of optical systems and even facilitating the generation of data across various modalities.

This Special Issue aims to present insights and breakthroughs from leading experts working in the multidisciplinary field of machine learning and Photonics, from improving health diagnostics in biomedical engineering and enhancing our understanding of biological processes in biophotonics to advancing our capabilities in remote sensing, refining techniques in ellipsometry, and exploring the depths of marine science.

Potential topics for this Special Issue include, but are not limited to, the following:

  • Machine learning for optical imaging systems.
  • Deep learning.
  • Super resolution.
  • Cross-modality image generation. 
  • Biomedical engineering.
  • Optical diagnostics.
  • Remote sensing.
  • Biomedical imaging.
  • Marine particle sensing.

All manuscripts will be considered pending peer review. Research articles, reviews, and perspectives are welcome.

Dr. Jiachen Wan
Dr. Yue Yao
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Photonics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • photonics
  • optics
  • biophotonics, biomedical imaging, machine learning
  • deep learning

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Published Papers (1 paper)

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Research

12 pages, 1965 KB  
Article
Quantifying Influence of Beam Drift on Linear Retardance Measurement in Dual-Rotating Retarder Mueller Matrix Polarimetry
by Kaisha Deng, Nan Zeng, Liangyu Deng, Shaoxiong Liu, Hui Ma, Chao He and Honghui He
Photonics 2025, 12(9), 868; https://doi.org/10.3390/photonics12090868 - 28 Aug 2025
Viewed by 705
Abstract
Mueller matrix polarimetry is recently attracting more and more attention for its diagnostic potentials. However, for prevalently used division of time Mueller matrix polarimeter based on dual-rotating retarder scheme, beam drift induced by rotating polarizers and waveplates introduces spatial misalignment and pseudo-edge artifacts [...] Read more.
Mueller matrix polarimetry is recently attracting more and more attention for its diagnostic potentials. However, for prevalently used division of time Mueller matrix polarimeter based on dual-rotating retarder scheme, beam drift induced by rotating polarizers and waveplates introduces spatial misalignment and pseudo-edge artifacts in imaging results, hindering following accurate microstructural features characterization. In this paper, we quantitatively analyze the beam drift phenomenon in dual-rotating retarder Mueller matrix microscopy and its impact on linear retardance measurement, which is frequently used to reflect tissue fiber arrangement. It is demonstrated that polarizer rotation induces larger beam drift than waveplate rotation due to surface non-uniformity and stress deformation. Furthermore, for waveplates rotated constantly in dual-rotating retarder scheme, their tilt within polarization state analyzer can result in more drift and throughput loss than those within polarization state generator. Finally, phantom and tissue experiments confirm that beam drift, rather than inherent optical path changes, dominates the systematic overestimation of linear retardance in boundary image regions. The findings highlight beam drift as a dominant error source for quantifying linear retardance, necessitating careful optical design alignment and a reliable registration algorithm to obtain highly accurate polarization data for training machine learning models of pathological diagnosis using Mueller matrix microscopy. Full article
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