STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Multimedia Technology & Recent Advancements Review Article

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

  1. Bahadur Singh Department of Computer Science Engineering, Sam College of Engineering and Technology Bhopal
  2. Sourabh Mandloi Department of Computer Science Engineering, Sam College of Engineering and Technology Bhopal
  3. Aashish Tiwari Department of Computer Science Engineering, Sam College of Engineering and Technology Bhopal

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

References (37)

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