Abstract
Abstract: This paper presents a comprehensive comparative analysis of deep learning-based methods and traditional techniques for image denoising and restoration. The objective is to evaluate the performance, computational efficiency, and generalizability of these methods across various types and levels of noise in images. This report presents a comprehensive comparative analysis of image denoising and restoration techniques, focusing on traditional methods and deep learning-based approaches. The study evaluates the performance, computational efficiency, and generalizability of these methods across various types and levels of noise in images.
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