Modern Methods of Image Processing and Comparative Analysis of These Methods

Abstract

Modern image processing encompasses a wide spectrum of techniques, from classical filtering and morphological operations to advanced deep learning models. Traditional methods rely on handcrafted algorithms (e.g. Gaussian smoothing, edge detection, feature descriptors) to enhance or analyze images, while deep learning approaches (such as Convolutional Neural Networks, segmentation networks, and generative models) learn representations directly from data. This article surveys key image processing methods - covering noise reduction, enhancement, segmentation, feature extraction and classification - and presents a comparative analysis of their strengths and limitations. A summary table highlights differences in computational cost, accuracy, and application domains. Findings indicate that deep learning methods achieve superior performance on complex tasks at the cost of data and computing requirements, whereas traditional methods remain efficient and interpretable for simpler tasks.