Review of Key Image Denoising Algorithms
Abstract
Images are one of the key sources of visual information and communication. It plays a crucial role in defense, AI, and forensic science, among others. However, it is prone to corruption from various types of noise from varying sources, mainly during acquisition and transmission. It creates artifacts or signal distortion due to statistical variance in easurements of pixel values responsible for contrast, color, or other aspects. Several denoising techniques exist, and many have been proposed to address it, but their performance is debatable. Noise leads to a loss of critical information, primarily in the form of edges, corners, which negatively affects performance. And there is no single, universal perfect solution to this problem. This paper reviews the existing techniques and analyzes the performance of three main techniques, namely Gaussian, linear isotropic, and non-linear isotropic smoothing. After careful examination, it is found that both Linear and Non-Linear smoothing can be an effective solution.
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