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Electronics
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Advanced Techniques in Real-Time Image Processing
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Advanced Techniques in Real-Time Image Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 3675

Editor

Dr. Lihui Chen

E-Mail Website
Guest Editor
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400054, China
Interests: image processing; artificial intelligence; remote sensing image analysis; visual-language model; deep learning

Special Issue Information

Dear Colleagues,

Image processing is a important technology for various application, including autorou driving, remote sensing analysis, target detection, and so on. However, in realistic application, the source of devices are usually limited, it is important to explore new methods in real-time image processing, facilitating the effective and efficient inference during the test stage. Therefore, in this Special Issue, we are interested in real-time image processing technologies, including, but not limited to, the following:

  1. nature image processing;
  2. remote image processing;
  3. medical image processing;
  4. image restoration;
  5. image super-resolution;
  6. image registration;
  7. image fusion;
  8. target recognition, classification, detection, segmentation
  9. lightweight design of image processing models;
  10. compression and quantitative of deep learning models;
  11. parallel computing for image processing.

Dr. Lihui Chen
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. Electronics is an international peer-reviewed open access semimonthly 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

  • real-time image processing
  • artificial intelligence
  • remote sensing image processing
  • medical image processing

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Published Papers (3 papers)

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Research

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18 pages, 36634 KB  
Article
Visibility Enhancement in Fire and Rescue Operations: ARMS Extension with Gaussian Estimation
by Jongpil Jeong, Myungjin Cho and Min-Chul Lee
Electronics 2026, 15(3), 667; https://doi.org/10.3390/electronics15030667 - 3 Feb 2026
Viewed by 679
Abstract
In fire and emergency rescue operations, visibility is often severely degraded by smoke, airborne debris, or atmospheric pollutants including smog and yellow dust. Several image restoration techniques, including Dark Channel Prior (DCP), Color Attribution Prior (CAP), Peplography, and Adaptive Removal via Mask for [...] Read more.
In fire and emergency rescue operations, visibility is often severely degraded by smoke, airborne debris, or atmospheric pollutants including smog and yellow dust. Several image restoration techniques, including Dark Channel Prior (DCP), Color Attribution Prior (CAP), Peplography, and Adaptive Removal via Mask for Scatter (ARMS), have been proposed to recover clear images under such conditions. However, these methods exhibit significant limitations in heavy scattering environments. This paper proposes a novel visibility restoration method for disaster situations, building upon the state-of-the-art ARMS method. To maximize the suppression of scattering effects, the Scattering Media Model is refined through Gaussian estimation. Additionally, an overlapping matrix is introduced to effectively handle non-uniformly distributed scattering conditions. The proposed method is evaluated using a real rescue operation image dataset provided by the Fire and Disaster Management Agency of Japan. Qualitative visual assessments and quantitative performance metrics demonstrate that the proposed approach significantly outperforms conventional methods under severe scattering conditions. Full article
(This article belongs to the Special Issue Advanced Techniques in Real-Time Image Processing)
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21 pages, 5078 KB  
Article
Parallelizable and Lightweight Reversible Data Hiding Framework for Encryption-Then-Compression Systems
by Ruifeng Li and Masaaki Fujiyoshi
Electronics 2026, 15(1), 136; https://doi.org/10.3390/electronics15010136 - 28 Dec 2025
Cited by 2 | Viewed by 685
Abstract
Encryption-then-compression (EtC) enables secure image processing while retaining coding efficiency. In grayscale-based EtC pipelines with YCbCr transformation and component serialization, reversible data hiding (RDH) becomes challenging because cross-channel correspondence is disrupted, and block-wise encryption operations (permutation, rotation, and brightness inversion) break embedding synchronization. [...] Read more.
Encryption-then-compression (EtC) enables secure image processing while retaining coding efficiency. In grayscale-based EtC pipelines with YCbCr transformation and component serialization, reversible data hiding (RDH) becomes challenging because cross-channel correspondence is disrupted, and block-wise encryption operations (permutation, rotation, and brightness inversion) break embedding synchronization. This paper presents a block-independent and lightweight RDH framework for such component-serialized grayscale EtC systems. The framework combines diagonal pixel absolute difference (DPAD)-based embedding with an encryption-invariant synchronization index (EISI), enabling reliable encrypted-domain extraction and self-synchronization under component serialization and block permutation, without auxiliary side information or any modification to the underlying EtC pipeline. All operations are performed locally at the block level, making the framework naturally parallelizable when needed. Experiments on standard datasets with diverse texture characteristics demonstrate reliable data extraction and perfect reversibility while preserving the structural properties required for secure encryption and lossless-mode compression. These results indicate that the proposed framework is well-suited to practical EtC deployments where lightweight implementation and block-level independence are essential. Full article
(This article belongs to the Special Issue Advanced Techniques in Real-Time Image Processing)
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Other

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33 pages, 3590 KB  
Systematic Review
Diffusion-Based Approaches for Medical Image Segmentation: An In-Depth Review
by Muhammad Yaseen, Maisam Ali, Sikandar Ali and Hee-Cheol Kim
Electronics 2026, 15(7), 1400; https://doi.org/10.3390/electronics15071400 - 27 Mar 2026
Viewed by 1715
Abstract
Medical image segmentation represents a fundamental task in medical image analysis, serving as a critical component for accurate diagnosis, treatment planning, and disease monitoring. The emergence of Denoising Diffusion Probabilistic Models (DDPMs) has revolutionized the landscape of generative modeling and recently gained significant [...] Read more.
Medical image segmentation represents a fundamental task in medical image analysis, serving as a critical component for accurate diagnosis, treatment planning, and disease monitoring. The emergence of Denoising Diffusion Probabilistic Models (DDPMs) has revolutionized the landscape of generative modeling and recently gained significant attention in medical image analysis. This comprehensive review examines the current state of the art in diffusion models for medical image segmentation, covering theoretical foundations, methodological innovations, computational efficiency strategies, and clinical applications. We analyze recent advances in latent diffusion frameworks, transformer-based architectures, and ambiguous segmentation modeling while addressing the practical challenges of implementing these models in clinical environments. The review encompasses applications across multiple medical imaging modalities including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), ultrasound, and X-ray imaging, providing insights into performance achievements and identifying future research directions. Through systematic analysis of publications mostly from 2019 to 2025, we demonstrate that diffusion models have achieved remarkable progress in addressing fundamental challenges including data scarcity, inter-observer variability, and uncertainty quantification. Notable achievements include inference time being reduced from 91.23 s to 0.34 s for echocardiogram segmentation (LDSeg, Echo dataset), DSC scores up to 0.96 for knee cartilage MRI segmentation, and a +13.87% DSC improvement over baseline methods for breast ultrasound segmentation. This review serves as a comprehensive resource for researchers and clinicians interested in leveraging diffusion models for medical image segmentation, providing a roadmap for future research and clinical translation. Full article
(This article belongs to the Special Issue Advanced Techniques in Real-Time Image Processing)
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