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Recent Advances in Efficient Image and Video Processing
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Recent Advances in Efficient Image and Video Processing

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

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 3609

Special Issue Editors

Prof. Dr. Ping Hu

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: computer vision; deep learning; video image analysis
Dr. Lu Zhang

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China
Interests: computer vision; deep learning; video image analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Donghyun Kim

E-Mail Website
Guest Editor
Department of AI, Korea University, Seoul 02841, Republic of Korea
Interests: computer vision; deep learning; transfer learning

Special Issue Information

Dear Colleagues,

The Special Issue on “Recent Advances in Efficient Image and Video Processing” aims to explore the latest developments in efficient models and learning techniques that address the ever-increasing demands of visual data processing. As the volume and complexity of image and video content continue to escalate across industries such as entertainment, healthcare, security, and communication, there is a pressing need for solutions that can deliver high performance while minimizing computational resources and data cost. This Special Issue will spotlight innovative approaches that address these demands, including the development of lightweight and scalable models, energy-efficient algorithms, and methods that accelerate training and inference processes without sacrificing accuracy. Additionally, it will delve into research on data-efficient learning techniques and optimization strategies that enable efficient processing in resource-constrained environments. We invite original contributions that present novel methodologies, theoretical insights, and practical implementations that push the boundaries of efficiency in image and video processing, catering to both academic research and real-world applications.

Prof. Dr. Ping Hu
Dr. Lu Zhang
Dr. Donghyun Kim
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. 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

  • efficient image/ video processing
  • lightweight models
  • real-time processing
  • data-efficient learning
  • energy-efficient algorithms

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Published Papers (3 papers)

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Research

23 pages, 10215 KB  
Article
Robust Denoising of Structure Noise Through Dual-Diffusion Brownian Bridge Modeling and Coupled Sampling
by Long Chen, Changan Yuan, Huafu Xu, Ye He and Jianhui Jiang
Electronics 2025, 14(21), 4243; https://doi.org/10.3390/electronics14214243 - 30 Oct 2025
Cited by 1 | Viewed by 1115
Abstract
Recent denoising methods based on diffusion models typically formulate the task as a conditional generation process initialized from a standard Gaussian distribution. However, such stochastic initialization often leads to redundant sampling steps and unstable results due to the neglect of structured noise characteristics. [...] Read more.
Recent denoising methods based on diffusion models typically formulate the task as a conditional generation process initialized from a standard Gaussian distribution. However, such stochastic initialization often leads to redundant sampling steps and unstable results due to the neglect of structured noise characteristics. To address these limitations, we propose a novel framework that directly bridges the probabilistic distributions of noisy and clean images while jointly modeling structured noise. We introduce Dual-diffusion Brownian Bridge Coupled Sampling (DBBCS) the first framework to incorporate Brownian bridge diffusion into image denoising. DBBCS synchronously models the distributions of clean images and structural noise via two coupled diffusion processes. Unlike conventional diffusion models, our method starts sampling directly from noisy observations and jointly optimizes image reconstruction and noise estimation through a coupled posterior sampling scheme. This allows for dynamic refinement of intermediate states by adaptively updating the sampling gradients using residual feedback from both image and noise paths. Specifically, DBBCS employs two parallel Brownian bridge models to learn the distributions of clean images and noise. During inference, their respective residual processes regulate each other to progressively enhance both denoising and noise estimation. A consistency constraint is enforced among the estimated noise, the reconstructed image, and the original noisy input to ensure stable and physically coherent results. Extensive experiments on standard benchmarks demonstrate that DBBCS achieves superior performance in both visual fidelity and quantitative metrics, offering a robust and efficient solution to image denoising. Full article
(This article belongs to the Special Issue Recent Advances in Efficient Image and Video Processing)
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19 pages, 5612 KB  
Article
DCPRES: Contrastive Deep Graph Clustering with Progressive Relaxation Weighting Strategy
by Xiao Qin, Lei Peng, Zhengyou Qin and Changan Yuan
Electronics 2025, 14(21), 4206; https://doi.org/10.3390/electronics14214206 - 28 Oct 2025
Viewed by 674
Abstract
Existing contrastive deep graph clustering methods typically employ fixed-threshold strategies when constructing positive and negative sample pairs, and fail to integrate both graph structure information and clustering structure information effectively. However, this fixed-threshold and binary partitioning approach is overly rigid, limiting the model’s [...] Read more.
Existing contrastive deep graph clustering methods typically employ fixed-threshold strategies when constructing positive and negative sample pairs, and fail to integrate both graph structure information and clustering structure information effectively. However, this fixed-threshold and binary partitioning approach is overly rigid, limiting the model’s utilization of potentially learnable samples. To address this problem, this paper proposes a contrastive deep graph clustering model with a progressive relaxation weighting strategy (DCPRES). By introducing the progressive relaxation weighting strategy (PRES), DCPRES dynamically allocates sample weights, constructing a progressive training strategy from easy to difficult samples. This effectively mitigates the impact of pseudo-label noise and enhances the quality of positive and negative sample pair construction. Building upon this, DCPRES designs two contrastive learning losses: an instance-level loss and a cluster-level loss. These respectively focus on local node information and global cluster distribution characteristics, promoting more robust representation learning and clustering performance. Extensive experiments demonstrated that DCPRES significantly outperforms existing methods on multiple public graph datasets, exhibiting a superior robustness and stability. For instance, on the CORA dataset, our model achieved a significant improvement over the static approach of CCGC, with the NMI increasing by 4.73%, the ACC by 4.77%, the ARI value by 7.03%, and the F1-score by 5.89%. It provides an efficient and stable solution for unsupervised graph clustering tasks. Full article
(This article belongs to the Special Issue Recent Advances in Efficient Image and Video Processing)
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18 pages, 1856 KB  
Article
A Uniform Multi-Modal Feature Extraction and Adaptive Local–Global Feature Fusion Structure for RGB-X Marine Animal Segmentation
by Yue Jiang, Yan Gao, Yifei Wang, Yue Wang, Hong Yu and Yuanshan Lin
Electronics 2025, 14(19), 3927; https://doi.org/10.3390/electronics14193927 - 2 Oct 2025
Viewed by 956
Abstract
Marine animal segmentation aims at segmenting marine animals in complex ocean scenes, which plays an important role in underwater intelligence research. Due to the complexity of underwater scenes, relying solely on a single RGB image or learning from a specific combination of multi-model [...] Read more.
Marine animal segmentation aims at segmenting marine animals in complex ocean scenes, which plays an important role in underwater intelligence research. Due to the complexity of underwater scenes, relying solely on a single RGB image or learning from a specific combination of multi-model information may not be very effective. Therefore, we propose a uniform multi-modal feature extraction and adaptive local–global feature fusion structure for RGB-X marine animal segmentation. It can be applicable to various situations such as RGB-D (RGB+depth) and RGB-O (RGB+optical flow) marine animal segmentation. Specifically, we first fine-tune the SAM encoder using parallel LoRA and adapters to separately extract RGB information and auxiliary information. Then, the Adaptive Local–Global Feature Fusion (ALGFF) module is proposed to progressively fuse multi-modal and multi-scale features in a simple and dynamical way. Experimental results on both RGB-D and RGB-O datasets demonstrate that our model achieves superior performance in underwater scene segmentation tasks. Full article
(This article belongs to the Special Issue Recent Advances in Efficient Image and Video Processing)
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