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Image Processing: Technologies, Methods, Apparatus

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 424

Special Issue Editors


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Guest Editor
School of Electrical Engineering, Jerusalem College of Technology, Jerusalem, Israel
Interests: Image processing

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Guest Editor
Laboratoire d’Analyse et d’Architecture des Systèmes (LAAS), LAMIA, Université des Antilles, 31400 Toulouse, France
Interests: image processing; bioinformatics; AI
Special Issues, Collections and Topics in MDPI journals

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Guest Editor

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Guest Editor
1. Department of Software Convergence, Gyeongkuk National University, 1375 Gyeongdong-ro, Andong-si 36729, Gyeongsangbuk-do, Republic of Korea
2. Department of Software Convergence, Andong National University, 1375 Gyeongdong-ro, Andong-si 36729, Gyeongsangbuk-do, Republic of Korea
Interests: information hiding; image security; visual symmetry; hybrid steganography; right-most digit replacement
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image processing is a basic tool for monitoring information in many everyday systems, such as cell phones, digital photography, the autonomous vehicle market, homeland security, and much more.

The wide distribution of optical sensors coincides with the ability to monitor information from different spectral fields and to process a lot of information in real-time.

Recently, the field of AI has also been applied to image processing in order to extract information inherent in optical systems.

Image processing is at the forefront of technological research, with significant breakthroughs. Several leading research directions can be noted, as reflected in publications in recent years:

(1) Increasing the sensing rate and the frame rate.
(2) Control of the optical spectrum, including information fusion.
(4) Self-processing in the detector.
(6) Image processing of SPAD and Lidar-based detectors.
(7) Image processing in space or marine environment.

Within this background, this Special Issue aims to provide a concise collection of studies dealing with image processing and provide important information to scientists and stakeholders for the development of technologies and systems based on sensors. Studies dealing with one or more of the following technologies are welcome: detector information extraction, multispectral analysis, and event-based detection. Furthermore, the physical modeling and deep learning-based analysis of data and data fusion techniques is highly encouraged for submission.

Articles and review articles may address, but are not limited to, the following topics:

  • Novel algorithms for image enhancement, segmentation, and recognition;
  • Machine learning and AI applications in image processing;
  • Computational imaging techniques;
  • Hardware and software innovations for image acquisition and analysis;
  • Biomedical, industrial, and security applications of image processing;
  • High-rate detector and an event detector.

Dr. Benjamin Milgrom
Prof. Dr. Andrei Doncescu
Prof. Dr. Cheonshik Kim
Prof. Dr. Ki-Hyun Jung
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. Applied Sciences 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
  • algorithms
  • machine learning

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Published Papers (1 paper)

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Research

19 pages, 3331 KiB  
Article
Low-Light Image Enhancement Using Deep Learning: A Lightweight Network with Synthetic and Benchmark Dataset Evaluation
by Manuel J. C. S. Reis
Appl. Sci. 2025, 15(11), 6330; https://doi.org/10.3390/app15116330 - 4 Jun 2025
Viewed by 246
Abstract
Low-light conditions often lead to severe degradation in image quality, impairing critical computer vision tasks in applications such as surveillance and mobile imaging. In this paper, we propose a lightweight deep learning framework for low-light image enhancement, designed to balance visual quality with [...] Read more.
Low-light conditions often lead to severe degradation in image quality, impairing critical computer vision tasks in applications such as surveillance and mobile imaging. In this paper, we propose a lightweight deep learning framework for low-light image enhancement, designed to balance visual quality with computational efficiency, with potential for deployment in latency-sensitive and resource-constrained environments. The architecture builds upon a UNet-inspired encoder–decoder structure, enhanced with attention modules and trained using a combination of perceptual and structural loss functions. Our training strategy utilizes a hybrid dataset composed of both real low-light images and synthetically generated image pairs created through controlled exposure adjustment and noise modeling. Experimental results on benchmark datasets such as LOL and SID demonstrate that our model achieves a Peak Signal-to-Noise Ratio (PSNR) of up to 28.4 dB and a Structural Similarity Index (SSIM) of 0.88 while maintaining a small parameter footprint (~1.3 M) and low inference latency (~6 FPS on Jetson Nano). The proposed approach offers a promising solution for industrial applications such as real-time surveillance, mobile photography, and embedded vision systems. Full article
(This article belongs to the Special Issue Image Processing: Technologies, Methods, Apparatus)
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