Nonlinear Variational Inequalities in Image Processing and Computer Vision

Abstract

Nonlinear Variational Inequalities (NVI) have emerged as powerful mathematical tools with wide-ranging applications in image processing and computer vision. This article explores the fundamental role of NVI in these fields, discussing their mathematical foundations, applications, and relevance. We delve into specific image processing techniques that leverage NVI, such as denoising, inpainting, and image segmentation. Additionally, we examine how NVI are employed in computer vision tasks like optical flow estimation and image registration. Through these applications, we illustrate the significance of NVI in enhancing the quality and efficiency of image analysis and manipulation.