Handbook of Convex Optimization Methods in Imaging Science

Discusses recent developments in imaging science and provides tools for solving image processing and computer vision problems using convex optimization methods

The reader is provided with the state of the art advancements in each imaging science problem that is covered and is directed to cutting edge theory and methods that should particularly help graduate students and young researchers in shaping their research

Each chapter of the book covers a real-world imaging science problem while balancing both the theoretical and experimental aspects. The theoretical foundation of the problem is discussed thoroughly and then from a practical point of view, extensive validation and experiments are provided to enable the transition from theory to practice

Topics of high current relevance are covered and include color and spectral imaging, dictionary learning for image classification and recovery, optimization and evaluation of image quality, sparsity constrained

estimation for image processing and computer vision etc Provides insight on handling real-world imaging science problems that involve hard and non-convex objective functions through tractable convex optimization methods with the goal of providing a favorable performance-complexity trade-off

estimation for image processing and computer vision etc Provides insight on handling real-world imaging science problems that involve hard and non-convex objective functions through tractable convex optimization methods with the goal of providing a favorable performance-complexity trade-off Discusses recent developments in imaging science and provides tools for solving image processing and computer vision problems using convex optimization methods The reader is provided with the state of the art advancements in each imaging science problem that is covered and is directed to cutting edge theory and methods that should particularly help graduate students and young researchers in shaping their research Each chapter of the book covers a real-world imaging science problem while balancing both the theoretical and experimental aspects. The theoretical foundation of the problem is discussed thoroughly and then from a practical point of view, extensive validation and experiments are provided to enable the transition from theory to practice Topics of high current relevance are covered and include color and spectral imaging, dictionary learning for image classification and recovery, optimization and evaluation of image quality, sparsity constrained

Autorentext
Vishal Monga is a tenured Associate Professor in the School of Electrical Engineering and Computer Science at the main campus of the Pennsylvania State University in University Park, PA. Prior to joining Penn State in Fall 2009, he worked at Xerox Research Labs from 2005-2009. He received his PhD from the Department of Electrical and Computer Engineering at the University of Texas, Austin in August 2005. He has also been a visiting researcher at Microsoft Research in Redmond, WA and a visiting faculty at the University of Rochester. Professor Monga's research in optimization methods for signal and image processing has been recognized and supported via a US National Science Foundation CAREER award. For his educational efforts, he received the 2016 Joel and Ruth Spira Teaching Excellence Award.

Inhalt
Preface.- 1 Introduction.- 2 Optimizing Image Quality.- 3 Computational Color Imaging.- 4 Optimization Methods for SAR.- 5 Computational Spectral Ultrafast Imaging.- 6 Discriminative Sparse Representation.- 7 Sparsity-based Nonlocal Image Restoration.- 8 Sparsity Constrained Estimation.- 9 Optimization Problems Associated with Manifolds.