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Electronics
Special Issues
Intelligent Image and Video Processing: Quality, Compression and Vision Applications
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Intelligent Image and Video Processing: Quality, Compression and Vision Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: 15 May 2026 | Viewed by 954

Special Issue Editor

Dr. Xian Tao

E-Mail Website
Guest Editor
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Interests: deep learning; electrode implantation robot for brain-computer interface; industrial vision detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent image and video processing regarding quality, compression and vision applications represent a paradigm shift in signal processing, driven by the synergistic innovation of deep learning and multi-modal fusion technologies. This transformation fundamentally redefines traditional theoretical frameworks and methodological systems, manifesting in three key dimensions: from local optimization to global perception in quality assessment systems, from general-purpose computing to edge intelligence in architectural paradigms, and from single-modal analysis to cross-modal understanding in cognitive approaches.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Image Quality Enhancement Techniques;
  • Low-Light/Super-Resolution Reconstruction;
  • Model Compression Paradigms;
  • Knowledge Distillation Techniques;
  • Visual Inspection and Measurement;
  • Surface Defect Detection;
  • Industrial Anomaly Detection;
  • Vision-Based Industrial Applications.

I/We look forward to receiving your contributions.

Dr. Xian Tao
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-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

  • image quality enhancement techniques
  • low-light/super-resolution reconstruction
  • model compression paradigms
  • knowledge distillation techniques
  • visual inspection and measurement
  • surface defect detection
  • industrial anomaly detection
  • vision-based industrial applications

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Published Papers (2 papers)

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Research

20 pages, 4373 KB  
Article
SO-YOLO11-CDP: An Instance Segmentation-Based Approach for Cross-Depth-of-Field Positioning Micro Image Sensor Modules in Precision Assembly
by Xi Lu, Juan Zhang, Yi Yang and Lie Bi
Electronics 2026, 15(2), 411; https://doi.org/10.3390/electronics15020411 - 16 Jan 2026
Viewed by 187
Abstract
During batch soldering, assembly of micro image sensor modules, initial random pose, and feature partially occlude target micro-component image, leading to issues of missed and erroneous detection, and low 3D spatial positioning accuracy due to cross-depth-of-field detection errors in microscopic vision. This paper [...] Read more.
During batch soldering, assembly of micro image sensor modules, initial random pose, and feature partially occlude target micro-component image, leading to issues of missed and erroneous detection, and low 3D spatial positioning accuracy due to cross-depth-of-field detection errors in microscopic vision. This paper proposes Small object-YOLO11-Cross-Depth-of-field Positioning (SO-YOLO11-CDP), an instance segmentation-based approach for precision cross-depth-of-field positioning micro-component. First, an improved Small object-YOLO11 (SO-YOLO11) image segmentation algorithm is designed. By incorporating a coordinate attention mechanism (CA) into segmentation head to enhance localization of micro-targets, the backbone uses non-stride convolution to preserve fine-grained feature, while target regression performance is boosted via Efficient-IoU (EIoU) loss combined with normalized Wasserstein distance (NWD). Subsequently, to further improve spatial position detection accuracy in cross-depth-of-field detection, a calibration error compensation model for image Jacobian matrix is established based on pinhole imaging principles. Experimental results indicate that SO-YOLO11 achieves 16.1% increase in precision, 4.0% increase in recall, and 9.9% increase in mean average precision (mAP0.5) over baseline YOLO11. Furthermore, it accomplishes spatial detection accuracy superior to 6.5 μm for target micro-components. The method presented in this paper holds significant engineering application value for high-precision spatial position detection of micro image sensor components. Full article
(This article belongs to the Special Issue Intelligent Image and Video Processing: Quality, Compression and Vision Applications)
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14 pages, 3077 KB  
Article
Visual Localization and Policy Learning for Robotic Large-Diameter Peg-in-Hole Assembly Tasks
by Tao Liang, Dingrong Wang, Wenzhi Ma, Lei Zhang and Dongsheng Chen
Electronics 2025, 14(23), 4592; https://doi.org/10.3390/electronics14234592 - 23 Nov 2025
Viewed by 505
Abstract
The conventional component assembly techniques employed in manufacturing industries typically necessitate laborious manual parameter calibration prior to system deployment, while existing vision-based control algorithms suffer from limited adaptability and inefficient learning capabilities. This paper presents a novel framework for automated large-diameter peg-in-hole assembly [...] Read more.
The conventional component assembly techniques employed in manufacturing industries typically necessitate laborious manual parameter calibration prior to system deployment, while existing vision-based control algorithms suffer from limited adaptability and inefficient learning capabilities. This paper presents a novel framework for automated large-diameter peg-in-hole assembly through convolutional network-based perception and reinforcement learning-driven control. Our methodology introduces three key innovations: (1) an enhanced deep segmentation architecture for precise identification and spatial localization of peg-end centroids, enabling accurate preliminary peg-in-hole; (2) a hybrid control strategy combining deep deterministic policy gradient (DDPG) reinforcement learning with classical control theory, augmented by real-time force feedback data acquisition; (3) systematic integration of visual–spatial information and haptic feedback for robust error compensation. Experimental validation on an industrial robotic platform demonstrates the method’s superior performance, achieving an Intersection over Union (IoU) score of 0.946 in peg segmentation tasks and maintaining insertion stability with maximum radial forces below 5.34 N during assembly operations. The proposed approach significantly reduces manual intervention requirements while exhibiting remarkable tolerance to positional deviations (±2.5 mm) and angular misalignments (±3°) commonly encountered in industrial assembly scenarios. Full article
(This article belongs to the Special Issue Intelligent Image and Video Processing: Quality, Compression and Vision Applications)
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