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Photonics
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Optical Imaging and Optical Image Processing: Advances in Systems, Algorithms,...
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Optical Imaging and Optical Image Processing: Advances in Systems, Algorithms, and Applications

A Special Issue of Photonics (ISSN 2304-6732) belonging to the section "Biophotonics and Biomedical Optics".

Deadline for manuscript submissions: 1 June 2027 | Viewed by 2466

Editors

Dr. Yiwei Chen

E-Mail
Guest Editor
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
Interests: optical coherence tomography images; adaptive optics; retinal imaging; vessel segmentation; vascular bundle; ophthalmology; age-related macular degeneration; convolutional neural network; diagnostic algorithm; deep learning; deep learning models
Dr. Zhenglong Sun

E-Mail Website
Guest Editor
Shenzhen Bay Laboratory, Shenzhen, China
Interests: intelligent medical optical imaging

Special Issue Information

Dear Colleagues,

Optical imaging has become a key enabling technology for observing, measuring, and interpreting structures and dynamics across biological, medical, industrial, environmental, and physical systems. At the same time, optical image processing is rapidly transforming the way optical data are reconstructed, enhanced, analyzed, and translated into quantitative information. This Special Issue aims to provide a focused forum for recent advances at the intersection of optical imaging systems and computational image processing methods.

We welcome original research articles, communications, and reviews covering novel optical imaging modalities, image reconstruction algorithms, enhancement and restoration methods, multimodal and hyperspectral imaging, phase and quantitative imaging, computational microscopy, optical coherence tomography, photoacoustic and fluorescence imaging, image registration, segmentation, classification, and deep learning for optical image analysis. Studies that integrate optical system design with algorithmic processing, improve imaging speed, resolution, sensitivity, robustness, or interpretability, or demonstrate practical applications in biomedical imaging, industrial inspection, remote sensing, machine vision, and photonic instrumentation are particularly encouraged.

By bringing together contributions from optics, photonics, signal processing, computer vision, and artificial intelligence, this Special Issue seeks to highlight emerging concepts, rigorous methodologies, and application-driven innovations that advance the acquisition, processing, and understanding of optical images.

Dr. Yiwei Chen
Dr. Zhenglong Sun
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-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

  • optical imaging
  • optical image processing
  • computational imaging
  • biomedical optical imaging
  • optical coherence tomography
  • hyperspectral and multispectral imaging
  • image reconstruction and enhancement
  • deep learning for optical imaging
  • quantitative phase imaging
  • photonic imaging systems

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Published Papers (4 papers)

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17 pages, 2818 KB  
Article
Polarization-Reliability-Adaptive Soft-Constrained Reconstruction of Relative 3D Morphology Under Ring Illumination
by Hesong Jiang, Zhenrui Hu and Juan Zhang
Photonics 2026, 13(10), 917; https://doi.org/10.3390/photonics13100917 - 28 Sep 2026
Viewed by 255
Abstract
Transparent cells often exhibit weak and spatially heterogeneous directional-intensity responses in non-interferometric microscopy, making morphology reconstruction vulnerable to low signal, incomplete angular information, and boundary artifacts. We present a polarization-reliability-adaptive soft-constrained ring-illumination method (PRAS-RIM) for reconstructing the apparent relative 3D morphology of transparent [...] Read more.
Transparent cells often exhibit weak and spatially heterogeneous directional-intensity responses in non-interferometric microscopy, making morphology reconstruction vulnerable to low signal, incomplete angular information, and boundary artifacts. We present a polarization-reliability-adaptive soft-constrained ring-illumination method (PRAS-RIM) for reconstructing the apparent relative 3D morphology of transparent samples. The principal acquisition configuration comprises a programmable 12-sector ring source (P12), a PET diffuser, a fixed circular polarizer, a 40× objective with NA = 0.65, and a division-of-focal-plane polarization camera. Calibrated directional intensity provides the primary geometric evidence, whereas the angle of polarization (AoP) is represented in double-angle form and incorporated only as a pixelwise reliability-weighted soft constraint. Across 29 microsphere measurements, complete PRAS-RIM reduced the mean object-level RMSE from 2.77 ± 0.19 μm for the intensity-only baseline to 0.59 ± 0.07 μm. A yeast test set comprising 51 cells from three distinct static fields acquired within the same experiment demonstrated cross-field consistency and spatially varying reliability, but was not used for absolute-height validation because no independent geometric ground truth was available. These results support PRAS-RIM as an interpretable framework for robust label-free reconstruction of relative morphology without claiming optical phase, optical path length, absolute cell height, or a three-dimensional refractive-index distribution. Full article
(This article belongs to the Special Issue Optical Imaging and Optical Image Processing: Advances in Systems, Algorithms, and Applications)
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16 pages, 2750 KB  
Article
Speckle Noise Reduction in OCT Retinal Images Based on a Multi-Scale Self-Attention Generative Adversarial Network
by Haiyi Bian, Yingzhou Zhu, Yeman Liu, Lei Liu, Xiaoteng Yan, Zhongjie Cheng and Yanrong Wang
Photonics 2026, 13(7), 682; https://doi.org/10.3390/photonics13070682 - 17 Jul 2026
Viewed by 585
Abstract
Optical coherence tomography (OCT) is widely used in biomedical imaging and ophthalmology. However, OCT images are frequently corrupted by speckle noise from coherent light interference. This degradation hampers clinical diagnosis of retinal lesions and identification of tissue layers. We propose a speckle noise [...] Read more.
Optical coherence tomography (OCT) is widely used in biomedical imaging and ophthalmology. However, OCT images are frequently corrupted by speckle noise from coherent light interference. This degradation hampers clinical diagnosis of retinal lesions and identification of tissue layers. We propose a speckle noise reduction algorithm based on a Multi-Scale Self-Attention Generative Adversarial Network combined with a Siamese network (SA-Siamese-GAN). To address the limited receptive fields of traditional convolutional neural networks (CNNs), which can cause broken or blurred retinal layers, a self-attention mechanism is integrated into the bottleneck layer of the generator to capture global pixel dependencies. A Siamese network enforces structural consistency, and a joint loss function combining Wasserstein distance, perceptual loss, and structural similarity (SSIM) is used. Experiments on 11,103 pairs of clinical OCT retinal images show that SA-Siamese-GAN outperforms BM3D, NLM, Wavelet, and GAN-ResNet. It removes speckle noise while preserving retinal layer structure and fine textures, and achieves the highest Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Full article
(This article belongs to the Special Issue Optical Imaging and Optical Image Processing: Advances in Systems, Algorithms, and Applications)
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17 pages, 1693 KB  
Article
Self-Prompting Segment Anything Model for Esophageal OCT Images
by Cong Wang and Meng Gan
Photonics 2026, 13(7), 664; https://doi.org/10.3390/photonics13070664 - 11 Jul 2026
Viewed by 567
Abstract
Optical coherence tomography (OCT) is an established imaging modality for esophageal disease assessment, enabling high-resolution visualization of tissue layers. However, automated segmentation of esophageal OCT images remains challenging due to low contrast, complex tissue structures, in vivo confounders, and scarce labeled clinical data. [...] Read more.
Optical coherence tomography (OCT) is an established imaging modality for esophageal disease assessment, enabling high-resolution visualization of tissue layers. However, automated segmentation of esophageal OCT images remains challenging due to low contrast, complex tissue structures, in vivo confounders, and scarce labeled clinical data. To address these issues, we propose a self-prompting segment anything model (SAM)-based one-shot segmentation framework tailored for esophageal OCT images. The framework integrates a pre-trained SAM encoder for general feature extraction and a self-pretrained prompt encoder to capture domain-specific features from unlabeled OCT data. Experiments on a self-collected mouse dataset and a public human dataset yielded Dice similarity coefficient (DSC) values exceeding 85%, with the highest mean DSC among the evaluated methods under the one-shot protocol, although greater performance dispersion was observed in the heterogeneous human cohort. These findings demonstrate the potential of combining unlabeled OCT pretraining with one labeled B-scan for annotation-efficient esophageal OCT segmentation. Full article
(This article belongs to the Special Issue Optical Imaging and Optical Image Processing: Advances in Systems, Algorithms, and Applications)
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16 pages, 4234 KB  
Article
SCUA-Net: Selective Contextual Uplift and Attention Network for Robust Infrared Small Target Detection in Complex Clutter
by Jiawei Lin, Xiaoyan Wang, Songjie Luo, Ziyang Chen, Xiaoyan Wu and Jixiong Pu
Photonics 2026, 13(7), 656; https://doi.org/10.3390/photonics13070656 - 8 Jul 2026
Viewed by 376
Abstract
Infrared small target detection (ISTD) remains challenging in complex cluttered environments because targets usually occupy only a few pixels and exhibit weak thermal radiation with limited texture information. The problem becomes more severe in high-resolution infrared imaging systems, where sliding-window inference is commonly [...] Read more.
Infrared small target detection (ISTD) remains challenging in complex cluttered environments because targets usually occupy only a few pixels and exhibit weak thermal radiation with limited texture information. The problem becomes more severe in high-resolution infrared imaging systems, where sliding-window inference is commonly adopted under memory and computational constraints. However, the truncated field of view may lead to contextual information loss and increased false alarms in cluttered regions. To address these issues, we propose the Selective Contextual Uplift and Attention Network (SCUA-Net). The proposed network adopts a U-Net++-style densely nested encoder–decoder architecture to enhance multi-scale feature interaction and preserve fine-grained weak-target features. In addition, a Global-Context Calibration Coordinate Attention (GCC-CA) module is introduced to inject window-level contextual statistics into coordinate attention, thereby improving clutter suppression and localization robustness under sliding-window inference. During training, a joint optimization strategy combining Online Hard Example Mining (OHEM) and Dice Loss is employed to alleviate severe foreground–background imbalance. During inference, Gaussian-weighted fusion is adopted to reduce stitching artifacts between adjacent windows. Experimental results on NUDT-SIRST and IRSTD-1k validate the effectiveness of the proposed method. SCUA-Net achieves 99.15% Pd, 0.558 × 10−6 Fa, and 0.9570 IoU on NUDT-SIRST, while maintaining competitive performance on IRSTD-1k at 161.6 FPS on an NVIDIA RTX 4090 platform, demonstrating favorable accuracy, robustness, and real-time performance in complex infrared scenarios. Full article
(This article belongs to the Special Issue Optical Imaging and Optical Image Processing: Advances in Systems, Algorithms, and Applications)
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