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Electronics
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Recent Advances and Applications in Image Restoration and Image Enhancement
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Recent Advances and Applications in Image Restoration and Image Enhancement

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

Deadline for manuscript submissions: 15 May 2026 | Viewed by 2342

Special Issue Editors

Dr. Almabrok Essa

E-Mail Website
Guest Editor
Department of Mathematics, Computer Science, and Data Science, John Carroll University, University Heights, OH 44118, USA
Interests: artificial intelligence; computer vision; machine learning; image processing; remote sensing; AR/VR
Dr. Sidike Paheding

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, School of Engineering of Computing, Fairfield University, 1073 N Benson Rd, Fairfield, CT 06824, USA
Interests: artificial intelligence; computer vision; machine learning; image processing; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The latest advancements, cutting-edge innovations, and applications in the fields of image restoration and image enhancement will be explored, addressing the critical challenges and breakthroughs in these rapidly evolving domains and showcasing the transformative impact of advanced computational techniques. This collection includes state-of-the-art research and innovative solutions that leverage techniques such as deep learning, generative models, and adaptive algorithms to improve image quality and fidelity. Therefore, this Special Issue emphasizes the growing impact of deep learning and generative algorithms in driving innovation in image processing. It also highlights interdisciplinary collaborations and future directions, such as real-time processing, energy-efficient algorithms for edge devices, generalization across diverse datasets, and ethical considerations. By providing a comprehensive view of the current progress and challenges, this Special Issue serves as a valuable resource for researchers, practitioners, and enthusiasts in image processing and computer vision. 

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

  • Advanced noise reduction and denoising techniques: novel algorithms for minimizing noise while preserving fine details in various imaging conditions.
  • Super-resolution methods: innovative methods for upscaling low-resolution images to high-quality outputs for applications in medical imaging, surveillance, and media production.
  • Deblurring solutions: cutting-edge solutions to mitigate motion blur, defocus, and other distortions.
  • Enhancement of visual features: techniques for improving contrast, color balance, and overall image aesthetics.
  • Emerging applications: real-world implementations in autonomous vehicles, remote sensing, medical diagnostics, artistic image editing, and security.

Dr. Almabrok Essa
Dr. Sidike Paheding
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

  • image enhancement
  • image restoration
  • image processing
  • super resolution
  • machine learning and deep learning

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Published Papers (3 papers)

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24 pages, 44361 KB  
Article
MIMAR-Net: Multiscale Inception-Based Manhattan Attention Residual Network and Its Application to Underwater Image Super-Resolution
by Nusrat Zahan, Sidike Paheding, Ashraf Saleem, Timothy C. Havens and Peter C. Esselman
Electronics 2025, 14(22), 4544; https://doi.org/10.3390/electronics14224544 - 20 Nov 2025
Viewed by 323
Abstract
In recent years, Single-Image Super-Resolution (SISR) has gained significant attention in the geoscience and remote sensing community for its potential to improve the resolution of low-quality underwater imagery. This paper introduces MIMAR-Net (Multiscale Inception-based Manhattan Attention Residual [...] Read more.
In recent years, Single-Image Super-Resolution (SISR) has gained significant attention in the geoscience and remote sensing community for its potential to improve the resolution of low-quality underwater imagery. This paper introduces MIMAR-Net (Multiscale Inception-based Manhattan Attention Residual Network), a new deep learning architecture designed to increase the spatial resolution of input color images. MIMAR-Net integrates a multiscale inception module, cascaded residue learning, and advanced attention mechanisms, such as the MaSA layer, to capture both local and global contextual information effectively. By utilizing multiscale processing and advanced attention strategies, MIMAR-Net allows us to handle the complexities of underwater environments with precision and robustness. We evaluate the model on three popular underwater image datasets, namely UFO-120, USR-248, and EUVP, and perform extensive comparisons against state-of-the-art methods. Experimental results demonstrate that MIMAR-Net consistently outperforms existing approaches, achieving superior qualitative and quantitative improvements in image quality, making it a reliable solution for underwater image enhancement in various challenging scenarios. Full article
(This article belongs to the Special Issue Recent Advances and Applications in Image Restoration and Image Enhancement)
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14 pages, 3884 KB  
Article
Enabling Super-Resolution Quantitative Phase Imaging via OpenSRQPI—A Standardized Plug-and-Play Open-Source Tool for Digital Holographic Microscopy with Structured and Oblique Illumination
by Sofia Obando-Vasquez, Alan Schneider and Ana Doblas
Electronics 2025, 14(22), 4513; https://doi.org/10.3390/electronics14224513 - 19 Nov 2025
Viewed by 537
Abstract
Accurate and label-free quantitative phase imaging (QPI) plays a crucial role in advancing diagnostic techniques that streamline histology and diagnostic procedures by minimizing sample preparation time, resources, and requirements. Although Digital Holographic Microscopy (DHM) has become a prominent tool within QPI, its diffraction-limited [...] Read more.
Accurate and label-free quantitative phase imaging (QPI) plays a crucial role in advancing diagnostic techniques that streamline histology and diagnostic procedures by minimizing sample preparation time, resources, and requirements. Although Digital Holographic Microscopy (DHM) has become a prominent tool within QPI, its diffraction-limited resolution has hindered broader adoption of QPI-DHM. The use of structured and oblique illumination in DHM platforms has overcome the resolution limit, advancing QPI-DHM technology to super-resolution QPI. Despite demonstrated success, adoption of super-resolution DHM (SR-DHM) in clinical and biomedical research remains limited by the absence of a standardized reconstruction algorithm capable of delivering quantitatively accurate, distortion-free super-resolved phase images. This work presents OpenSRQPI, the first standardized computational framework for super-resolution phase reconstruction in DHM systems, whether using structured or oblique illumination. Through its intuitive graphical user interface (GUI) and minimal parameter requirements, OpenSRQPI reduces the technical barrier for non-experts, making super-resolution QPI broadly accessible, enabling new studies of live-cell dynamics, subcellular structure, and tissue morphology. Full article
(This article belongs to the Special Issue Recent Advances and Applications in Image Restoration and Image Enhancement)
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15 pages, 1774 KB  
Article
FreqSpatNet: Frequency and Spatial Dual-Domain Collaborative Learning for Low-Light Image Enhancement
by Yu Guan, Mingsi Liu, Xi’ai Chen, Xudong Wang and Xin Luan
Electronics 2025, 14(11), 2220; https://doi.org/10.3390/electronics14112220 - 29 May 2025
Cited by 2 | Viewed by 951
Abstract
Low-light images often contain noise due to the conditions under which they are taken. Fourier transform can reduce this noise in frequency while preserving the image detail embedded in the low-frequency components. Existing low-light image-enhancement methods based on CNN frameworks often fail to [...] Read more.
Low-light images often contain noise due to the conditions under which they are taken. Fourier transform can reduce this noise in frequency while preserving the image detail embedded in the low-frequency components. Existing low-light image-enhancement methods based on CNN frameworks often fail to extract global feature information and introduce excessive noise, resulting in detail loss. To solve the above problems, we propose a low-light image-enhancement framework and achieve detail restoration and denoising by using Fourier transform. In addition, we design a dual-domain enhancement strategy, which cooperatively utilizes global frequency-domain feature extraction to improve the overall brightness of the image and the amplitude modulation of the spatial-domain convolution operation to perform local detail refinement to improve the quality of the image by suppressing noise, enhancing the contrast, and preserving the texture at the same time. Extensive experiments on low-light datasets show that our results outperform mainstream methods, especially in maintaining natural color distributions and recovering fine-grained details under extreme lighting conditions. We adopted two evaluation indicators, PSNR and SSIM. Our method improved the PSNR by 4.37% compared to the Restormer method and by 1.76% compared to the DRBN method. Full article
(This article belongs to the Special Issue Recent Advances and Applications in Image Restoration and Image Enhancement)
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