A Review of Computational Methods for Image Restoration and Noise Reduction Using Reference Images

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 13 | 01 | Page :
    By

    Bahadur Singh,

  • Sourabh Mandloi,

  • Aashish Tiwari,

  1. Research Scholar, Department of Computer Science Engineering, Sam College of Engineering and Technology, Bhopal, Madhya Pradesh, India
  2. Head of Department, Department of Computer Science Engineering, Sam College of Engineering and Technology, Bhopal, Madhya Pradesh, India
  3. Professor, Department of Computer Science Engineering, Sam College of Engineering and Technology, Bhopal, Madhya Pradesh, India

Abstract

Digital imaging systems play a vital role in numerous application domains, including consumer photography, biomedical imaging, remote sensing, aerial surveillance, and astronomical observation. Despite continuous advancements in imaging hardware and software, the visual data acquired by these systems often suffer from degradation caused by spatially non-uniform blur. This blur may arise due to several factors, such as lens imperfections, atmospheric turbulence, sensor limitations, motion between the camera and the scene, and various post- processing operations. The presence of such degradation significantly affects image interpretability and overall visual quality, making accurate blur estimation a crucial and challenging research problem. Both global and local blur estimation are essential for effectively addressing spatially varying blur and restoring image fidelity. Reliable blur estimation not only enables improved image restoration but also provides meaningful information about the underlying scene structure. For instance, blur characteristics can be exploited to infer depth cues, object boundaries, and regions of visual saliency. Motivated by these considerations, this study investigates a transformation-based image restoration framework to mitigate blur-related degradation. Transformation-driven techniques have been shown in prior studies to effectively preserve fine structural details while suppressing noise and unwanted artifacts. Building upon these insights, the proposed approach leverages transformation-domain analysis to enhance edge information, recover lost textures, and improve overall restoration performance. Experimental analysis demonstrates that the method effectively handles spatially varying blur and produces visually improved results, highlighting its potential for advanced imaging applications.

Keywords: Digital, Imaging systems, Algorithms, Visual quality, Restoration, fine features

How to cite this article:
Bahadur Singh, Sourabh Mandloi, Aashish Tiwari. A Review of Computational Methods for Image Restoration and Noise Reduction Using Reference Images. Journal of Multimedia Technology & Recent Advancements. 2026; 13(01):-.
How to cite this URL:
Bahadur Singh, Sourabh Mandloi, Aashish Tiwari. A Review of Computational Methods for Image Restoration and Noise Reduction Using Reference Images. Journal of Multimedia Technology & Recent Advancements. 2026; 13(01):-. Available from: https://journals.stmjournals.com/jomtra/article=2026/view=242102


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Ahead of Print Subscription Review Article
Volume 13
01
Received 25/01/2026
Accepted 28/01/2026
Published 20/03/2026
Publication Time 54 Days
40

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