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Electronics
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Image Fusion and Image Processing
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Image Fusion and Image Processing

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

Deadline for manuscript submissions: 20 June 2025 | Viewed by 3485

Special Issue Editors

Dr. Jiang He

E-Mail Website
Guest Editor
Data Science in Earth Observation, Technical University of Munich, 80333 Bavaria, Germany
Interests: image fusion; spectral super-resolution; machine learning
Prof. Dr. Liang-Jian Deng

E-Mail Website
Guest Editor
School of Mathematical Sciences，University of Electronic Science and Technology of China, Chengdu 610056, China
Interests: image fusion; computer vision; partial differential equation; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Renwei Dian

E-Mail Website
Guest Editor
College of Electrical and Information Engineering, Hunan University, Changsha 410012, China
Interests: hyperspectral image super-resolution; image fusion; tensor decomposition

Special Issue Information

Dear Colleagues,

Individual images often suffer from various quality issues, limiting their effectiveness. In addition, single images may fail to capture all necessary details due to the inherent constraints of imaging devices, leading to incomplete or fragmented information. To address these challenges, image fusion and image processing have emerged as crucial techniques. Image fusion integrates complementary information from multiple sources, providing a more comprehensive and accurate representation. Concurrently, image processing enhances image quality by reducing distortions and improving resolution. These technologies play a pivotal role in enhancing the reliability and efficiency of image-based communication and information retrieval, offering significant potential in applications such as medical imaging, remote sensing, and multimedia, where precise and detailed image interpretation is critical. This Special Issue invites contributions focusing on advancements in image fusion, quality improvement, semantic extraction, and the identification of emerging challenges in image processing. We seek novel solutions that push the boundaries of image information extraction.

Topics of interest include, but are not limited to:

  • Multi-focus, multi-exposure, and multi-angle fusion;
  • Spatial–spectral, multi-hyperspectral, and spatio–temporal fusion in remote sensing;
  • Denoising, dehazing, cloud removal, and super-resolution in image processing;
  • Object detection, classification, and tracking in image analysis.

Dr. Jiang He
Prof. Dr. Liang-Jian Deng
Dr. Renwei Dian
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 fusion
  • image quality improvement
  • image restoration
  • image super-resolution
  • computer vision

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Published Papers (3 papers)

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Research

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28 pages, 5356 KiB  
Article
Temporal Adaptive Attention Map Guidance for Text-to-Image Diffusion Models
by Sunghoon Jung and Yong Seok Heo
Electronics 2025, 14(3), 412; https://doi.org/10.3390/electronics14030412 - 21 Jan 2025
Viewed by 998
Abstract
Text-to-image generation aims to create visually compelling images aligned with input prompts, but challenges such as subject mixing and subject neglect, often caused by semantic leakage during the generation process, remain, particularly in multi-subject scenarios. To mitigate this, existing methods optimize attention maps [...] Read more.
Text-to-image generation aims to create visually compelling images aligned with input prompts, but challenges such as subject mixing and subject neglect, often caused by semantic leakage during the generation process, remain, particularly in multi-subject scenarios. To mitigate this, existing methods optimize attention maps in diffusion models, using static loss functions at each time step, often leading to suboptimal results due to insufficient consideration of varying characteristics across diffusion stages. To address this problem, we propose a novel framework that adaptively guides the attention maps by dividing the diffusion process into four intervals: initial, layout, shape, and refinement. We adaptively optimize attention maps using interval-specific strategies and a dynamic loss function. Additionally, we introduce a seed filtering method based on the self-attention map analysis to detect and address the semantic leakage by restarting the generation process with new noise seeds when necessary. Extensive experiments on various datasets demonstrate that our method achieves significant improvements in generating images aligned with input prompts, outperforming previous approaches both quantitatively and qualitatively. Full article
(This article belongs to the Special Issue Image Fusion and Image Processing)
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16 pages, 9114 KiB  
Article
Low-Rank Tensor Recovery Based on Nonconvex Geman Norm and Total Variation
by Xinhua Su, Huixiang Lin, Huanmin Ge and Yifan Mei
Electronics 2025, 14(2), 238; https://doi.org/10.3390/electronics14020238 - 8 Jan 2025
Viewed by 804
Abstract
Tensor restoration finds applications in various fields, including data science, image processing, and machine learning, where the global low-rank property is a crucial prior. As the convex relaxation to the tensor rank function, the traditional tensor nuclear norm is used by directly adding [...] Read more.
Tensor restoration finds applications in various fields, including data science, image processing, and machine learning, where the global low-rank property is a crucial prior. As the convex relaxation to the tensor rank function, the traditional tensor nuclear norm is used by directly adding all the singular values of a tensor. Considering the variations among singular values, nonconvex regularizations have been proposed to approximate the tensor rank function more effectively, leading to improved recovery performance. In addition, the local characteristics of the tensor could further improve detail recovery. Currently, the gradient tensor is explored to effectively capture the smoothness property across tensor dimensions. However, previous studies considered the gradient tensor only within the context of the nuclear norm. In order to better simultaneously represent the global low-rank property and local smoothness of tensors, we propose a novel regularization, the Tensor-Correlated Total Variation (TCTV), based on the nonconvex Geman norm and total variation. Specifically, the proposed method minimizes the nonconvex Geman norm on singular values of the gradient tensor. It enhances the recovery performance of a low-rank tensor by simultaneously reducing estimation bias, improving approximation accuracy, preserving fine-grained structural details and maintaining good computational efficiency compared to traditional convex regularizations. Based on the proposed TCTV regularization, we develop TC-TCTV and TRPCA-TCTV models to solve completion and denoising problems, respectively. Subsequently, the proposed models are solved by the Alternating Direction Method of Multipliers (ADMM), and the complexity and convergence of the algorithm are analyzed. Extensive numerical results on multiple datasets validate the superior recovery performance of our method, even in extreme conditions with high missing rates. Full article
(This article belongs to the Special Issue Image Fusion and Image Processing)
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Review

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21 pages, 1062 KiB  
Review
An Overview of Quantum Circuit Design Focusing on Compression and Representation
by Ershadul Haque, Manoranjan Paul, Faranak Tohidi and Anwaar Ulhaq
Electronics 2025, 14(1), 72; https://doi.org/10.3390/electronics14010072 - 27 Dec 2024
Viewed by 1098
Abstract
Quantum image computing has attracted attention due to its vast storage capacity and faster image data processing, leveraging unique properties such as parallelism, superposition, and entanglement, surpassing classical computers. Although classical computing power has grown substantially over the last decade, its rate of [...] Read more.
Quantum image computing has attracted attention due to its vast storage capacity and faster image data processing, leveraging unique properties such as parallelism, superposition, and entanglement, surpassing classical computers. Although classical computing power has grown substantially over the last decade, its rate of improvement has slowed, struggling to meet the demands of massive datasets. Several approaches have emerged for encoding and compressing classical images on quantum processors. However, a significant limitation is the complexity of preparing the quantum state, which translates pixel coordinates into corresponding quantum circuits. Current approaches for representing large-scale images require higher quantum resources, such as qubits and connection gates, presenting significant hurdles. This article aims to overview the pixel intensity and state preparation circuits requiring fewer quantum resources and explore effective compression techniques for medium and high-resolution images. It also conducts a comprehensive study of quantum image representation and compression techniques, categorizing methods by grayscale and color image types and evaluating their strengths and weaknesses. Moreover, the efficacy of each model’s compression can guide future research toward efficient circuit designs for medium- to high-resolution images. Furthermore, it is a valuable reference for advancing quantum image processing research by providing a systematic framework for evaluating quantum image compression and representation algorithms. Full article
(This article belongs to the Special Issue Image Fusion and Image Processing)
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