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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). This special issue belongs to the section "Biophotonics and Biomedical Optics".

Deadline for manuscript submissions: 1 June 2027 | Viewed by 428

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 (1 paper)

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Research

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 (registering DOI) - 8 Jul 2026
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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