Study and Analysis of Image Processing Algorithms

Abstract

Image processing plays a critical role in transforming raw visual data into meaningful information across numerous applications, including medical diagnostics, surveillance, and autonomous vehicles. This paper provides an exhaustive study and comparative analysis of various image processing algorithms. We begin by discussing classical algorithms that focus on pixel-level operations such as filtering, edge detection, and segmentation. Subsequently, we explore modern approaches that utilize machine learning and deep learning techniques, particularly convolutional neural networks (CNNs) and generative adversarial networks (GANs). This paper highlights the strengths, limitations, and practical applications of these algorithms, while also outlining current challenges and future research directions in the field.