Optimization Techniques in Computer Vision: Ill-Posed Problems and Regularization

This comprehensive volume delves into the intricate landscape of optimization techniques applied to computer vision, specifically focusing on the challenges posed by ill-posed problems. The authors, Mongi A. Abidi, Andrei V. Gribok, and Joonki Paik, bring together their expertise to explore the theoretical underpinnings and practical applications of various regularization methods. Each chapter is crafted to enhance the reader's understanding, showcasing how optimization plays a pivotal role in solving real-world issues faced by researchers and practitioners in the field.

As the book progresses, it addresses the nuances of formulating and resolving ill-posed problems, offering insights into innovative regularization techniques. The authors balance theoretical discussions with practical implementations, providing illustrative examples that highlight the significance of optimization in improving image processing tasks. This scholarly work serves as a critical resource for academia and industry alike, attracting anyone who seeks to deepen their knowledge of computer vision methodologies and advancements.