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Photonics
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Deep Learning in Optical Engineering: Applications in Design, Simulation, and...
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Deep Learning in Optical Engineering: Applications in Design, Simulation, and Performance

A special issue of Photonics (ISSN 2304-6732). This special issue belongs to the section "Data-Science Based Techniques in Photonics".

Deadline for manuscript submissions: 1 October 2026 | Viewed by 1293

Editor

Dr. Dongliang Zheng

E-Mail Website
Guest Editor
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: optical measurement; 3D reconstruction; pattern recognition; machine vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning has injected intelligent impetus into the entire chain of optical engineering, forging close connections among the core links of optical material research and development, system design, processing and manufacturing, and testing. On the material front, it enables precise prediction of key parameters such as refractive index, thereby expediting the screening of novel functional optical materials. In the design phase, it achieves inverse intelligent design of optical devices, surmounting the limitations of traditional forward design methodologies. During the processing stage, it dynamically adjusts process parameters based on visual monitoring, effectively minimizing surface form errors. In the field of optical inspection, it facilitates rapid identification of micro-defects in components and accurate analysis of imaging performance indicators, significantly boosting testing efficiency and precision. This addresses the inherent drawbacks of traditional testing methods, such as strong subjectivity and high missed detection rates, and provides a robust guarantee for the quality control of optical products.

Dr. Dongliang Zheng
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • deep learning
  • R&D of optical materials
  • optical design
  • optical inspection

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Published Papers (3 papers)

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Research

21 pages, 2528 KB  
Article
Improving Precision in Extended-Range Three-Dimensional Single-Molecule Localization with Physics-Guided Deep Learning
by Xiang Zhou, Yuma Ito and Makio Tokunaga
Photonics 2026, 13(7), 649; https://doi.org/10.3390/photonics13070649 - 3 Jul 2026
Abstract
Extended-range three-dimensional (3D) single-molecule localization microscopy (SMLM) and single-particle tracking (SPT) require precise emitter localization across cellular-scale axial distances. However, long-rangeengineered point-spread functions (PSFs) spread photons over wider camera footprints, lowering the signal-to-noise ratio (SNR) and localization precision. We numerically evaluated a physics-guided [...] Read more.
Extended-range three-dimensional (3D) single-molecule localization microscopy (SMLM) and single-particle tracking (SPT) require precise emitter localization across cellular-scale axial distances. However, long-rangeengineered point-spread functions (PSFs) spread photons over wider camera footprints, lowering the signal-to-noise ratio (SNR) and localization precision. We numerically evaluated a physics-guided deep learning workflow for 3D localization over a 10.0 µm axial range using simulated electron-multiplying charge-coupled device (EMCCD) images. The workflow combines an analytical secondary-astigmatism phase mask, frequency-domain cross-filtering, a cross-filtering generative adversarial network (CFGAN), and coarse-to-fine fitting. The optical model and engineered PSF provide physical signal priors, cross-filtering preserves directional Fourier-domain energy, and CFGAN suppresses residual structured noise before model-based localization. In low-SNR simulations, lateral, axial, and radial root-mean-squared localization errors (RMSEs) decreased from 54.11, 96.12, and 112.79 nm without denoising to 31.14, 39.06, and 50.12 nm after CFGAN denoising—close to Cramér–Rao lower-bound (CRLB) references of 34.39, 38.94, and 51.95 nm. High-SNR RMSE values were 8.78, 12.00, and 14.96 nm, comparable to CRLB references of 10.36, 11.71, and 15.64 nm. These simulations suggest that physics-guided restoration can improve extended-range 3D SMLM precision, while experimental validation remains necessary. Full article
(This article belongs to the Special Issue Deep Learning in Optical Engineering: Applications in Design, Simulation, and Performance)
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19 pages, 10120 KB  
Article
Noise-Robust Loop-Based Deep Optical Convolutional Neural Network
by Maryam Dehbashizadeh Chehreghan and Ripalta Stabile
Photonics 2026, 13(6), 552; https://doi.org/10.3390/photonics13060552 - 4 Jun 2026
Viewed by 398
Abstract
We demonstrate a loop-based deep optical convolutional neural network that reuses a single free-space optical hardware to realize network depth through repeated passes. Convolution is implemented with programmable SLM with Fourier plane kernels, nonlinearity is provided by the photorefractive phase-only response of a [...] Read more.
We demonstrate a loop-based deep optical convolutional neural network that reuses a single free-space optical hardware to realize network depth through repeated passes. Convolution is implemented with programmable SLM with Fourier plane kernels, nonlinearity is provided by the photorefractive phase-only response of a BSO crystal and converted to an effective intensity activation via spatial filtering, and pooling is performed optically using demagnified imaging with an iris. On MNIST, the BSO-based nonlinearity improves test accuracy from 90.8% (linear) to 95.7%, with optimal operation. We model realistic optical noises (laser fluctuation, aberration, detector misalignment, and dust) and compare them using an SSIM-normalized severity metric. Under noise at (s = 0.35) on Fashion-MNIST, accuracy drops from 88.53% (clean) to 79.5% (noisy inference); a feature-level noise-aware training strategy recovers performance to 86.87%. Together, these advances demonstrate that a compact, loop-based hybrid DOCNN, completed with simple optical nonlinearities, simplified pooling, and noise-aware learning, can improve accuracy under realistic conditions. Full article
(This article belongs to the Special Issue Deep Learning in Optical Engineering: Applications in Design, Simulation, and Performance)
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26 pages, 11735 KB  
Article
Neural Network-Driven Transmission Characteristics Modeling and Manufacturing Error Detection for Photonic Lanterns
by Zhuruixiang Sun, Xiang Li, Yao Lu, Tong Liu, Zilun Chen and Zongfu Jiang
Photonics 2026, 13(5), 496; https://doi.org/10.3390/photonics13050496 - 16 May 2026
Viewed by 370
Abstract
Traditional numerical simulation methods struggle to accurately characterize the transmission characteristics of finished photonic lanterns that contain manufacturing errors. This paper proposes a method for characterizing photonic lantern devices using neural networks. In an ideal 1×6 photonic lantern, the Mean Squared [...] Read more.
Traditional numerical simulation methods struggle to accurately characterize the transmission characteristics of finished photonic lanterns that contain manufacturing errors. This paper proposes a method for characterizing photonic lantern devices using neural networks. In an ideal 1×6 photonic lantern, the Mean Squared Error (MSE) for predicting intensity from the multimode to the single-mode end was reduced to 10−5, and the neural network model can identify manufacturing error patterns, providing a new approach to addressing the precise characterization and product screening of novel irregular waveguides such as photonic lanterns. Full article
(This article belongs to the Special Issue Deep Learning in Optical Engineering: Applications in Design, Simulation, and Performance)
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