Evaluation of noise reduction methods in digital images: Classical filtering vs adaptive learning-based models

Abstract

Digital images acquired through imaging systems often lose their original fidelity due to various types of noise introduced during acquisition, storage, or transmission, consequently reducing clarity and adversely affecting subsequent image-processing tasks. Conventional noise-filtering approaches, such as Wiener, median, and Gaussian filtering, have been widely used because they are easy to implement and provide reasonably consistent performance. At the same time, these techniques have inflexible limits in stabilizing edges and small pixel-based details, especially when noise levels are high. In contrast, recent evolution in adaptive learning-based techniques, including convolutional based neural networks and denoising autoencoders, has employed more productive results for image restoration. This research undertakes a systematic comparison between classical filtering and modern adaptive denoising models using various levels of image datasets affected with Gaussian, salt-and-pepper, and speckle noise. Evaluation metrics, such as peak signal-to-noise ratio, structural similarity index, mean squared error, and qualitative visual observation, are used to assess performance. The findings reveal that learning-based models always exceed traditional filters, specifically in preserving structural information, texture details, and overall image fidelity. While adaptive models achieve superior restoration quality, they demand greater computational resources, which may limit their applicability in low-power or real-time settings. Overall, the research results validate that adaptive learning based techniques are well chosen for high-precision imaging results where each and every detail preservation is important. The research reinforces the significance of selecting denoising strategies corresponding to the particular requirement and constraints of the proposed application.