Handbook of Convex Optimization Methods in Imaging Science

Herausgegeben:Monga, Vishal

• Broschiertes Buch

Produktbeschreibung

This book covers recent advances in image processing and imaging sciences from an optimization viewpoint, especially convex optimization with the goal of designing tractable algorithms. Throughout the handbook, the authors introduce topics on the most key aspects of image acquisition and processing that are based on the formulation and solution of novel optimization problems. The first part includes a review of the mathematical methods and foundations required, and covers topics in image quality optimization and assessment. The second part of the book discusses concepts in image formation and capture from color imaging to radar and multispectral imaging. The third part focuses on sparsity constrained optimization in image processing and vision and includes inverse problems such as image restoration and de-noising, image classification and recognition and learning-based problems pertinent to image understanding. Throughout, convex optimization techniques are shown to be a criticallyimportant mathematical tool for imaging science problems and applied extensively.

Convex Optimization Methods in Imaging Science is the first book of its kind and will appeal to undergraduate and graduate students, industrial researchers and engineers and those generally interested in computational aspects of modern, real-world imaging and image processing problems.

Produktdetails

• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-319-87121-9
• Softcover reprint of the original 1st ed. 2018
• Seitenzahl: 248
• Erscheinungstermin: 24. August 2018
• Englisch
• Abmessung: 254mm x 178mm x 14mm
• Gewicht: 478g
• ISBN-13: 9783319871219
• ISBN-10: 3319871218
• Artikelnr.: 55129204

Autorenporträt

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.

Inhaltsangabe

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.