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Photonics
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AI for Photonics: Intelligent Imaging, Learning-Driven Optics, and Photonic Computing
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AI for Photonics: Intelligent Imaging, Learning-Driven Optics, and Photonic Computing

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: 31 July 2026 | Viewed by 6707

Special Issue Editors

Prof. Dr. Jiawei Sun

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Guest Editor
Jiangsu Key Laboratory for Advanced Theranostics and Medical Instrumentation, Suzhou Institute of Biomedical Engineering and Technology (SIBET), Chinese Academy of Sciences, Suzhou 215163, China
Interests: AI for photonics; lensless fiber endomicroscopy; digital holography; optical trapping; optical tomography; wavefront shaping
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhaohong Liu

E-Mail Website
Guest Editor
Center For Advanced Laser Technology (CALT), Hebei University of Technology, Xiping Street 5340, Tianjin 300401, China
Interests: nonlinear optics; high power laser; stimulated Brillouin scattering; solid-state laser; spectral imaging; microimaging
Special Issues, Collections and Topics in MDPI journals
Dr. Zeyu Gao

E-Mail Website
Guest Editor
Key Laboratory on Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Science, Chengdu, China
Interests: adaptive optics; wavefront sensing; laser communication; flow measurements
Special Issues, Collections and Topics in MDPI journals
Dr. Qian Zhang

E-Mail Website
Guest Editor
Laboratory of Measurement and Sensor System Technique, TU Dresden, Helmholtzstrasse 18, 01069 Dresden, Sachsen, Germany
Interests: intelligent photonics; physics-informed deep learning; multimode fiber; optical computing; wavefront shaping; optical communication

Special Issue Information

Dear Colleagues,

AI for photonics replaces handcrafted optics with learning-driven design and closed-loop control, delivering faster development and robust performance in complex environments. It enables inverse-designed devices, adaptive wavefront shaping, intelligent imaging, and reliable control of nonlinear/high-power lasers—shrinking the gap from lab prototypes to real-world systems.

This Special Issue on “AI for Photonics: Intelligent Imaging, Learning-Driven Optics, and Photonic Computing” welcomes basic, methodological, and applied contributions, as regular and review papers, covering (but not limited to):

  • Development and validation of AI-enhanced microscopic or imaging instruments;
  • Modelling of light–matter interactions and learning-based methods;
  • Processing of multidimensional optical data using machine-learning methods;
  • Development of multimodal, multispectral, and multiscale approaches;
  • Advancement of adaptive optics and wavefront shaping in complex media and links;
  • Optical computing methods or optical neural networks.

We look forward to contributions that connect rigorous physical modelling with data-driven intelligence, and that demonstrate compelling progress from fundamental concepts to robust, real-world photonic systems.

Dr. Jiawei Sun
Dr. Zhaohong Liu
Dr. Zeyu Gao
Dr. Qian Zhang
Guest Editors

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-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

  • AI for photonics and learning-driven optics
  • inverse design and differentiable photonics
  • adaptive optics and wavefront sensing or shaping
  • digital holography and computational imaging
  • lensless and fiber endomicroscopy
  • optical tomography and quantitative phase imaging
  • optical neural networks and photonic computing
  • nonlinear optics and high-power or solid-state lasers
  • stimulated Brillouin scattering and Raman sensing
  • physics-informed machine learning and digital twins

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (8 papers)

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Research

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37 pages, 6067 KB  
Article
SCISA-Net: Scene-Constrained Inverse-to-Subband Attention for Semantic Inference from Wall-Mediated Indirect Observations
by Jihao Dai, Hongshuai Qin, Guowen Li, Jin Liu, Xiaoshuai Zhang, Huiyu Qi, Zhiwen Zheng and Xingru Huang
Photonics 2026, 13(6), 575; https://doi.org/10.3390/photonics13060575 - 11 Jun 2026
Abstract
We study whether the semantic category of a hidden display terminal can be inferred from a wall-mediated indirect observation when the display remains outside the camera field of view under a controlled and calibrated scene configuration. This setting provides a security-motivated feasibility test [...] Read more.
We study whether the semantic category of a hidden display terminal can be inferred from a wall-mediated indirect observation when the display remains outside the camera field of view under a controlled and calibrated scene configuration. This setting provides a security-motivated feasibility test for indirect optical semantic leakage, but it remains challenging for two reasons. First, indirect propagation makes the wall pattern dominated by the occluder contour, while category-bearing evidence survives only as weak radiometric variations, making stable extraction difficult. Second, even after front-end recovery, low-frequency support is relatively stable, whereas the mid- and high-frequency details required for class separation remain weak and distortion-prone; as a result, the classifier may drift toward dominant but weakly informative coarse-grained patterns and fail to consistently accumulate fine-grained discriminative cues. We propose SCISA-Net, which combines scene-constrained inversion with multi-stage Haar-subband attention to reorganize indirect observations, compensate residual feature degradation, and aggregate class-relevant subband evidence. Experiments on a paired 31-class benchmark show stable recognition, robustness to illumination attenuation and ambient background interference, matched scene-operator re-parameterization capability, and clear degradation when key inverse or subband components are disrupted. These results support the feasibility of category-level semantic inference from calibrated wall-mediated indirect observations. Full article
(This article belongs to the Special Issue AI for Photonics: Intelligent Imaging, Learning-Driven Optics, and Photonic Computing)
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11 pages, 2575 KB  
Article
Wavelet-Fused Deep Learning for Computational Phase Correction in Dual-Comb Ranging
by Yao Li, Yuwei Cai, Zhongjian Gao, Wen Ren and Zili Zhang
Photonics 2026, 13(5), 506; https://doi.org/10.3390/photonics13050506 - 21 May 2026
Viewed by 262
Abstract
Dual-comb ranging enables rapid, high-precision absolute distance measurements, but its performance is constrained by intrinsic phase noise, which induces temporal jitter and degrades pulse-to-pulse mutual coherence. Here, we propose a deep learning network Wavelet-Fused DenseNet (WFDNet) for hardware-free computational phase correction in dual-comb [...] Read more.
Dual-comb ranging enables rapid, high-precision absolute distance measurements, but its performance is constrained by intrinsic phase noise, which induces temporal jitter and degrades pulse-to-pulse mutual coherence. Here, we propose a deep learning network Wavelet-Fused DenseNet (WFDNet) for hardware-free computational phase correction in dual-comb ranging. Through integration of complex wavelet decomposition and physics-guided feature encoding, the network, trained on model-generated data, can directly extract multi-scale time–frequency features to correct phase distortions and recover temporal coherence of the signals. Results from both simulated and experimental scenarios reveal that the approach can effectively suppress spectral noise and retrieve robust and unambiguous phases information, achieving high ranging accuracy with a standard deviation of 0.6 μm. Full article
(This article belongs to the Special Issue AI for Photonics: Intelligent Imaging, Learning-Driven Optics, and Photonic Computing)
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17 pages, 7609 KB  
Article
Plasma Physics-Based Deep Learning Modeling for Accurate Morphology Prediction in Femtosecond Bessel Laser Processing of ZnS
by Yifan Deng, Jingya Sun, Manlou Ye, Xiaokang Dong, Xiang Li and Yang Yang
Photonics 2026, 13(4), 394; https://doi.org/10.3390/photonics13040394 - 20 Apr 2026
Viewed by 802
Abstract
Femtosecond laser processing has become a powerful approach for high-precision micro- and nanofabrication in transparent materials, owing to its ultrashort pulse duration and minimized thermal effects. However, the limited predictability of processing depth remains a major obstacle to practical applications. Here, we present [...] Read more.
Femtosecond laser processing has become a powerful approach for high-precision micro- and nanofabrication in transparent materials, owing to its ultrashort pulse duration and minimized thermal effects. However, the limited predictability of processing depth remains a major obstacle to practical applications. Here, we present a morphology prediction framework for femtosecond Bessel laser processing of ZnS that integrates plasma physics modeling with deep learning. Through combined experimental measurements and plasma physics simulations, the influence of laser pulse energy on electron density evolution and material removal depth is systematically investigated. The results reveal the dominant roles of multiphoton ionization, avalanche ionization, and free-electron dynamics in deep-volume processing, and demonstrate the strong sensitivity of the processing morphology to the plasma distribution. Conventional plasma models can accurately reproduce the ablation diameter, yet exhibit significant limitations in predicting the processing depth. We propose a physics data-based framework for femtosecond Bessel beam processing, which integrates a depth-residual regression network conditioned on the peak electron density distribution to effectively learn and compensate for systematic modeling errors in plasma-based simulations. This strategy leads to excellent agreement between predicted and experimental processing depths and three-dimensional morphologies under various energy conditions. The model achieves a mean absolute error (MAE) of 4.9 nm at the pixel level for 3D crater reconstruction. Under rigorous crater-grouped cross-validation with Leave-One-Group-Out evaluation, the model achieves a mean R2 of 0.74 across 8 independent craters, demonstrating reliable generalization to unseen energy conditions. These results demonstrate that incorporating physical priors into data-driven learning provides an effective pathway to overcoming accuracy limitations in modeling complex laser–matter interactions. This approach offers a reliable tool for quantitative prediction and parameter optimization in deep femtosecond laser processing of transparent materials and enabling highly controllable and reproducible micro- and nanofabrication for advanced photonic and three-dimensional optical applications. Full article
(This article belongs to the Special Issue AI for Photonics: Intelligent Imaging, Learning-Driven Optics, and Photonic Computing)
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9 pages, 1265 KB  
Communication
Deep Learning-Assisted Design of All-Dielectric Micropillar Quantum Well Infrared Photodetectors
by Pengzhe Xia, Rui Xin, Tianxin Li and Wei Lu
Photonics 2026, 13(4), 381; https://doi.org/10.3390/photonics13040381 - 16 Apr 2026
Viewed by 525
Abstract
The integration of micro-nano optical structures has become an essential strategy for overcoming the performance bottlenecks of quantum well infrared photodetectors (QWIPs), specifically by addressing the inherent inability of planar devices to couple with normally incident light due to intersubband transition selection rules. [...] Read more.
The integration of micro-nano optical structures has become an essential strategy for overcoming the performance bottlenecks of quantum well infrared photodetectors (QWIPs), specifically by addressing the inherent inability of planar devices to couple with normally incident light due to intersubband transition selection rules. A critical factor in this integration is the precise spectral overlap between an optical mode and the material’s excitation mode. Therefore, achieving precise spectral engineering is indispensable. However, conventional electromagnetic simulations act as forward solvers, calculating optical responses based on given geometric parameters. They cannot directly perform inverse design, which involves deriving optimal geometric parameters directly from a desired optical response. Consequently, structural optimization is severely constrained by time-consuming trial-and-error iterations, which often struggle to find the global optimum in a complex design space. To overcome these limitations, this paper presents a comprehensive theoretical and numerical study proposing a deep learning framework for QWIPs coupled with all-dielectric micropillar structures. By establishing a structure-absorption spectrum dataset via finite difference time domain (FDTD) simulations, we developed a dual-network setup. For the forward prediction, a multilayer perceptron (MLP) maps geometric parameters (side length a and period p) to the absorption spectrum, achieving a computational speedup of seven orders of magnitude over traditional numerical simulations. Concurrently, a convolutional neural network (CNN) is employed for the inverse design, realizing on-demand design of geometric parameters based on target spectra with high reconstruction accuracy. Furthermore, the selected all-dielectric micropillar structures are highly compatible with mainstream semiconductor fabrication processes. This research provides an efficient, automated toolkit for the development of high-performance infrared photodetectors. Full article
(This article belongs to the Special Issue AI for Photonics: Intelligent Imaging, Learning-Driven Optics, and Photonic Computing)
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21 pages, 23671 KB  
Article
Zero-Shot Polarization-Intensity Physical Fusion Monocular Depth Estimation for High Dynamic Range Scenes
by Renhao Rao, Zhizhao Ouyang, Shuang Chen, Liang Chen, Guoqin Huang and Changcai Cui
Photonics 2026, 13(3), 268; https://doi.org/10.3390/photonics13030268 - 11 Mar 2026
Viewed by 606
Abstract
Monocular 3D reconstruction remains a persistent challenge for autonomous driving systems in Degraded Visual Environments (DVEs) with extreme glare and low illumination, such as highway tunnels, due to the lack of reliable texture cues. This paper proposes a physics-aware deep learning framework that [...] Read more.
Monocular 3D reconstruction remains a persistent challenge for autonomous driving systems in Degraded Visual Environments (DVEs) with extreme glare and low illumination, such as highway tunnels, due to the lack of reliable texture cues. This paper proposes a physics-aware deep learning framework that overcomes these limitations by fusing polarization sensing with conventional intensity imaging. Unlike traditional end-to-end data-driven fusion strategies, we propose a Modality-Aligned Parameter Injectionstrategy. By remapping the weight space of the input layer, this strategy achieves a smooth transfer of the pre-trained Vision Transformer (i.e., MiDaS) to multi-modal inputs. Its core advantage lies in the seamless integration of four-channel polarization geometric information while fully preserving the pre-trained semantic representation capabilities of the backbone network, thereby avoiding the overfitting risk associated with training from scratch on small-sample data. Furthermore, we design a Reliability-Aware Gating mechanism that dynamically re-weights appearance and geometric cues based on intensity saturation and the physical validity of polarization signals as measured by the Degree of Linear Polarization (DoLP). We validate the proposed method on our self-constructed POLAR-GLV benchmark, a real-world dataset collected specifically for high dynamic range tunnel scenarios. Extensive experiments demonstrate that our method consistently outperforms intensity-only baselines, reducing geometric reconstruction error by 24.2% in high-glare tunnel exit zones and 10.0% at tunnel entrances. Crucially, compared to multi-stream fusion architectures, these performance gains come with negligible additional computational cost, making the framework highly suitable for resource-constrained onboard inference environments. Full article
(This article belongs to the Special Issue AI for Photonics: Intelligent Imaging, Learning-Driven Optics, and Photonic Computing)
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11 pages, 2081 KB  
Article
Noise-Correlated Neural Network Channel Selection for Signal-to-Noise Ratio Enhancement in Holographic Data Storage
by Junqian Deng, Dakui Lin, Xiao Lin and Xiaodi Tan
Photonics 2026, 13(2), 126; https://doi.org/10.3390/photonics13020126 - 29 Jan 2026
Viewed by 564
Abstract
Neural networks significantly outperform traditional methods in both decoding amplitude-, phase-, and polarization-encoded data pages and suppressing noise within them. However, the mechanism behind neural networks’ denoising capability remains not fully understood. We discover that zeroing channels can improve the reconstruction effect of [...] Read more.
Neural networks significantly outperform traditional methods in both decoding amplitude-, phase-, and polarization-encoded data pages and suppressing noise within them. However, the mechanism behind neural networks’ denoising capability remains not fully understood. We discover that zeroing channels can improve the reconstruction effect of the model. Consequently, this paper presents a method to locate the noise feature objectively from γ, the weights of the Batch Normalization (BN) layer. γ stands for the importance of the channel in the model and γ < 1 means the channel may contain noise feature. Through experiments, removing the channels that contained a higher proportion of these noisy features, the reconstructed data pages showed a ~2% improvement in Peak Signal-to-Noise Ratio (PSNR) compared to results obtained by directly outputting data without removing the noisy channels. It indicates that neural networks achieve efficient denoising of encoded data pages by adjusting the weight parameters of BN layers, thereby suppressing or enhancing specific channels. Full article
(This article belongs to the Special Issue AI for Photonics: Intelligent Imaging, Learning-Driven Optics, and Photonic Computing)
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Review

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21 pages, 3893 KB  
Review
Progress in Spectral Information Processing Technology for Brillouin Microscopy
by Zhaohong Liu, Xiaoxuan Li, Xiaorui Sun, Zihan Yu, Yunjun Gao, Yun Zhang, Yu Zhou, Qiang Su, Yuanqing Xia, Yulei Wang and Zhiwei Lv
Photonics 2026, 13(1), 36; https://doi.org/10.3390/photonics13010036 - 31 Dec 2025
Viewed by 959
Abstract
This paper systematically reviews the key spectral information extraction methods in Brillouin microscopy, aiming to address the core challenge of accurately extracting material mechanical parameters from raw spectra. Based on technical principles, the methods are categorized into three types for elaboration: Spontaneous Brillouin [...] Read more.
This paper systematically reviews the key spectral information extraction methods in Brillouin microscopy, aiming to address the core challenge of accurately extracting material mechanical parameters from raw spectra. Based on technical principles, the methods are categorized into three types for elaboration: Spontaneous Brillouin Scattering (SpBS) is characterized by low signal-to-noise ratio (SNR) and strong background interference, and its processing relies on high-precision spectrometers and complex preprocessing procedures to mitigate noise and background effects; Stimulated Brillouin Scattering (SBS) operates on the mechanism of optical gain/loss, which achieves significantly improved data SNR and thereby enables more robust and accurate Lorentzian fitting for spectral analysis; Impulsive Stimulated Brillouin Scattering (ISBS) retrieves the frequency spectrum by inverting time-domain oscillating signals, and the core of its processing lies in super-resolution algorithms such as Fast Fourier Transform (FFT) and the Matrix Pencil Method, which are tailored to match its high-speed data acquisition capability. The paper further compares the advantages and disadvantages of various methods, outlines future development trends of intelligent processing technologies such as deep learning and multi-modal data fusion, and provides a clear guide for selecting the optimal data processing strategy in different application scenarios. Full article
(This article belongs to the Special Issue AI for Photonics: Intelligent Imaging, Learning-Driven Optics, and Photonic Computing)
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20 pages, 26650 KB  
Review
Advancements in Optical Diffraction Neural Networks
by Tianyu Han, Jiawei Sun and Xibin Yang
Photonics 2025, 12(12), 1187; https://doi.org/10.3390/photonics12121187 - 2 Dec 2025
Cited by 1 | Viewed by 2343
Abstract
Optical diffraction neural networks (ODNNs) represent a promising advancement in computational optics, with significant potential for applications in image classification, image reconstruction, and biomedical imaging. By using the principles of light diffraction for neural network computations, ODNNs enable low-power, real-time data processing without [...] Read more.
Optical diffraction neural networks (ODNNs) represent a promising advancement in computational optics, with significant potential for applications in image classification, image reconstruction, and biomedical imaging. By using the principles of light diffraction for neural network computations, ODNNs enable low-power, real-time data processing without the need for traditional electronic computing units. This review provides an overview of the foundational concepts behind ODNNs, starting with the principles of artificial neurons and progressing to the specific implementation of optical diffraction in neural network architectures. We examine recent advancements in key components of ODNNs, including optical signal processing, activation functions, and training algorithms. Additionally, we highlight the practical applications of ODNNs in areas such as signal analysis, optical imaging, image processing, and high-dimensional optical communications. This paper concludes with a discussion of the current challenges and future directions for ODNN research, emphasizing the potential for overcoming existing limitations and further expanding their capabilities. Full article
(This article belongs to the Special Issue AI for Photonics: Intelligent Imaging, Learning-Driven Optics, and Photonic Computing)
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