Unleashing the Power of Image Denoising: A Comprehensive Review of Classical to Deep Learning Methods

Abstract

Image denoising is a vital step in many image processing and computer vision professions that aims to improve picture quality by decreasing noise while maintaining important image information. In this article, a detailed overview is presented of traditional and deep learning based picture denoising approaches. Classical methods, such as linear filtering, transform domain techniques, and patch-based approaches like Non-Local Means (NLM), are commonly employed because they are simple and effective in removing Gaussian noise. However, these approaches frequently struggle with complicated noise patterns and blur fine features. Recent advances in deep learning, including Convolutional Neural Networks (CNNs) and other designs such as Generative Adversarial Networks (GANs) and autoencoders residual networks, have considerably improved picture denoising performance. These data-driven techniques excel in learning complicated noise patterns from big datasets, providing superior generalization across various noise types, including non-Gaussian noise, and dealing with a larger range of image degradation conditions.