Image processing techniques for detection of objects in blurry pictures: A comprehensive review

  • Ghassan Abdullah Abdulwasea Al-Maamari orcid

    Computer Information Systems Department, College of Information Technology and Computer Science, University of Saba Region, Marib, Yemen
  • Mubarak Mohammed Al-Ezzi Sufyan orcid

    Department of Computer Information Systems, Al-Jawf Faculty, University of Saba Region, Marib, Yemen
  • Ramzi Hamid Abdo Al-Jaberi orcid

    Computer Information Systems Department, College of Information Technology and Computer Science, University of Saba Region, Marib, Yemen
  • Mokhtar H. Al-Sarori orcid

    Computer Information Systems Department, College of Information Technology and Computer Science, University of Saba Region, Marib, Yemen
  • Mahfoudh Al-Asaly orcid

    Department of Information Technology, College of Computer, Qassim University, Buraydah 51174, Saudi Arabia
  • Asma’a Khalil Alkershi orcid

    Department of Information Technology, College of Information Technology and Computer Science, University of Saba Region, Marib, Yemen
Article ID: 3991
DOI: https://doi.org/10.59400/cai3991
Keywords: blurry image processing, image deblurring, object detection, image enhancement, deep learning, super-resolution, convolutional neural networks (CNN), YOLO

Abstract

Detecting objects in blurry and degraded images remains a critical unsolved challenge in computer vision, affecting applications from medical diagnostics and autonomous navigation to remote sensing and surveillance. Image degradation caused by motion, defocus, poor lighting, or environmental factors severely compromises feature visibility and limits the performance of conventional detection algorithms. This paper presents a comprehensive, systematic review of state-of-the-art techniques designed to address this problem. We first categorize common image degradations and analyze classical and deep learning-based solutions for image deblurring and enhancement, including CNN (Convolutional Neural Networks), GAN (Generative Adversarial Networks), and transformer architectures. The review then critically examines object detection models, particularly YOLO (You Only Look Once) and CNN-based networks, adapted for low-quality inputs. A key focus is on integrated pipelines that jointly optimize restoration and detection. We synthesize findings from over 200 studies, highlighting performance across diverse domains such as UAV (Unmanned Aerial Vehicle) imagery, underwater exploration, and medical analysis. Furthermore, we discuss standard datasets and evaluation metrics, identify persistent challenges including real-time processing, multi-degradation handling, and domain adaptation, and outline promising research directions. This review serves as a foundational resource for researchers and practitioners aiming to build robust vision systems for real-world, blur-prone environments.

Published
2026-03-16
How to Cite
Al-Maamari, G. A. A., Sufyan, M. M. A.-E., Al-Jaberi, R. H. A., Al-Sarori, M. H., Al-Asaly, M., & Alkershi, A. K. (2026). Image processing techniques for detection of objects in blurry pictures: A comprehensive review. Computing and Artificial Intelligence, 4(1). https://doi.org/10.59400/cai3991
Issue
Vol. 4 No. 1 (2026)
Section
Review

Copyright (c) 2026 Ghassan Abdullah Abdulwasea Al-Maamari, Mubarak Mohammed Al-Ezzi Sufyan, Ramzi Hamid Abdo Al-Jaberi, Mokhtar H. Al-Sarori, Mahfoudh Al-Asaly, Asma’a Khalil Alkershi

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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