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Digital image quality is essential for a variety of applications in visual information processing, including medical diagnostics, defence, and many other applications. However, there are several challenges that must be overcome before an image can be used, and these often result in noise, distortion, and degradation. The field of image restoration is growing in significance as it focuses on repairing and restoring damaged images. With the increasing number of imaging equipment and the wide range of applications, image restoration techniques are becoming more and more important. Because of the challenges posed by low light levels, sensor noise, and other environmental limitations, more analysis on restoration strategies is needed. This study provides researchers, practitioners, and students with a clear understanding of image restoration techniques by providing an extensive overview of the developments in different fields. This research study offers a comprehensive analysis by analysing the development of these techniques, from traditional methodologies to the most recent deep learning approaches. Here we have proposed a novel architecture for image restoration, designed to facilitate seamless implementation and achieve high-quality results.
Pawar et al. (Wed,) studied this question.