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Electronics
Special Issues
Image Processing: From Datasets to Segmentation, Classification and Detection
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Image Processing: From Datasets to Segmentation, Classification and Detection

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

Deadline for manuscript submissions: 15 August 2025 | Viewed by 875

Special Issue Editors

Dr. Wei Ke

E-Mail Website
Guest Editor
School of Software Engineering, Xi’an Jiaotong University, 710049 Xi’an, China
Interests: computer vision; machine learning
Dr. Lin Chen

E-Mail Website
Guest Editor
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 400714 Chongqing, China
Interests: image processing; pattern recognition
Dr. Yuhang He

E-Mail Website
Guest Editor
College of Artificial Intelligence, Xi’an Jiaotong University, 710049 Xi’an, China
Interests: video tracking; continual learning; object detection

Special Issue Information

Dear colleagues,

This Special Issue focuses on image processing, which is a fundamental and essential technology that has revolutionized various fields such as visual recognition, medical imaging, etc. This Special Issue delves into the core concepts of image processing, encompassing datasets, segmentation, classification, and detection. Datasets are used to train models and algorithms to recognize patterns and features in images, especially for data-driven training in the era of deep learning. Segmentation is the process of partitioning an image into multiple segments to simplify and change the representation of the image. Classification is the identification of different objects or regions in an image based on their features, and detection is the process of identifying and locating objects or regions of interest within an image. Benefiting from deep learning, all of the above topics have been widely studied. However, there is still a considerable gap in image processing compared to human perception. The main aim of this Special Issue is to seek high-quality submissions to advance the research and practical applications of image processing.

The topics of interest include, but are not limited to, the following:

  • Image enhancement, restoration, and denoising;
  • Image super resolution;
  • Image classification;
  • Image retrieval;
  • Semantic segmentation and instance segmentation;
  • Object detection and recognition;
  • Multi-modality information processing;
  • Medical image processing;
  • Applications of image processing;
  • Benchmarking studies and the development of new datasets.

We sincerely welcome you to submit your manuscripts for consideration in this Special Issue, contributing to the collective advancement of image processing research and its applications.

Dr. Wei Ke
Dr. Lin Chen
Dr. Yuhang He
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 processing
  • classification
  • object detection
  • object segmentation
  • medical image processing

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Published Papers (1 paper)

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Research

18 pages, 18506 KiB  
Article
NCSBFF-Net: Nested Cross-Scale and Bidirectional Feature Fusion Network for Lightweight and Accurate Remote-Sensing Image Semantic Segmentation
by Shihao Zhu, Binqiang Zhang, Dawei Wen and Yuan Tian
Electronics 2025, 14(7), 1335; https://doi.org/10.3390/electronics14071335 - 27 Mar 2025
Viewed by 365
Abstract
Semantic segmentation has emerged as a critical research area in Earth observation. This paper proposes a novel end-to-end semantic segmentation network, the Nested Cross-Scale and Bidirectional Feature Fusion Network (NCSBFF-Net), to address issues such as intra-class heterogeneity, inter-class homogeneity, scale variability, and the [...] Read more.
Semantic segmentation has emerged as a critical research area in Earth observation. This paper proposes a novel end-to-end semantic segmentation network, the Nested Cross-Scale and Bidirectional Feature Fusion Network (NCSBFF-Net), to address issues such as intra-class heterogeneity, inter-class homogeneity, scale variability, and the classification of tiny objects. Specifically, a CNN-based lightweight feature pyramid module is employed to extract contextual information across multiple scales, thereby addressing intra-class heterogeneity. The NCSBFF module leverages features from both shallow and deep layers and is designed to fuse multi-scale features, thereby enhancing inter-class semantic differences. Additionally, the shallowest feature is passed to the Shuffle Attention block in the NCSBFF module, which adaptively filters out weak details and highlights critical information for the classification of tiny objects. Extensive experiments were conducted on the Potsdam and Vaihingen benchmarks. Experiment results demonstrate that the NCSBFF-Net outperforms state-of-the-art methods, achieving a better trade-off between accuracy and efficiency, with a 5% improvement in mIoU significantly enhancing the recognition capability of small and complex objects, such as vehicles and irregular land parcels, in challenging scenes, and a 1.73% increase in accuracy demonstrating a better balance between computational efficiency and segmentation accuracy, providing an optimized solution for deployment on edge devices. Full article
(This article belongs to the Special Issue Image Processing: From Datasets to Segmentation, Classification and Detection)
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