Comparative Analysis of Existing Image Processing Methods

Image processing methods have evolved significantly, from traditional, rule-based approaches to modern machine learning techniques that leverage large datasets and complex algorithms. This paper compares conventional and contemporary image processing methods by examining their fundamental principles, applications, advantages, and limitations. Traditional methods, such as edge detection, segmentation, and filtering, are efficient and easy to interpret. In contrast, modern deep learning-based approaches demonstrate superior performance on complex tasks and with large datasets. This survey analyzes the strengths and weaknesses of both approaches to provide insights into their suitability for various applications. We conclude by discussing the potential for integrating these methods to develop hybrid solutions for more robust image processing.