723616795

Super Resolution of Images and Video / Aggelos K. Katsaggelos (Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois, USA), Rafael Molina, (Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad de Granada, Granada, Spain), Javier Mateos (Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad de Granada, Granada, Spain)

Molina, Rafael [VerfasserIn] ; Mateos, Javier [VerfasserIn]

Englisch

[San Rafael] : Morgan & Claypool Publishers, [2007]

© 2007

1 Online-Ressource (150 Seiten)

Description based upon print version of record

Mode of access: World Wide Web

System requirements: PDF reader

Print version: Super Resolution of Images and Video

1-59829-085-1

978-1-59829-085-1

Weitere Ausgaben: 1-59829-084-3 (Druckausgabe), 978-1-59829-084-4 (Druckausgabe)

DOI: 10.2200/S00036ED1V01Y200606IVM007

Introduction; What is super resolution of images and video?; Why and when is super resolution possible?; Applications; Book outline; Bayesian Formulation of Super-Resolution Image Reconstruction; Notation; Bayesian modeling; Bayesian inference; Hierarchical Bayesian Modeling and Inference; Low-Resolution Image Formation Models; Image Formation Models for Uncompressed Observations; The Warp--Blur Model; The Blur--Warp Model; Image Formation Models for Compressed Observations; Limits on super resolution; Motion Estimation in Super Resolution; Motion Estimation from Uncompressed Observations

Motion Estimation from Compressed ObservationsHow to Detect Unreliable Motion Estimates; Consistency of Motion Estimates for super resolution ; Some Open Issues in Motion Estimation for super resolution; Estimation of High-Resolution Images; High-resolution image estimation from uncompressed sequences; High-Resolution image estimation from compressed sequences; Some open issues in image estimation for super resolution; Bayesian Inference Models in Super Resolution; Hierarchical Bayesian Framework for Super Resolution; Inference models for super-resolution reconstruction problems

Some Open Issues in super resolution Bayesian InferencePre- and Post-Processing of Video Sequences; Including super resolution into the Compression Scheme; Region-Based Super Resolution for Compression; Motion and texture segmentation; Downsampling process; Upsampling procedure

*Electronic books ; Video compression -- Mathematical models ; Image reconstruction -- Mathematical models ; Resolution (Optics) -- Mathematical models ; Image processing -- Digital techniques -- Mathematical models ; Image processing -- Digital techniques

Klassifikation der Library of Congress: TA1637 .K38 2007

Dewey Dezimal-Klassifikation: 621.36;

This book focuses on the super resolution of images and video. The authors' use of the term super resolution (SR) is used to describe the process of obtaining a high resolution (HR) image, or a sequence of HR images, from a set of low resolution (LR) observations. This process has also been referred to in the literature as resolution enhancement (RE). SR has been applied primarily to spatial and temporal RE, but also to hyperspectral image enhancement. This book concentrates on motion based spatial RE, although the authors also describe motion free and hyperspectral image SR problems. Also examined is the very recent research area of SR for compression, which consists of the intentional downsampling, during pre-processing, of a video sequence to be compressed and the application of SR techniques, during post-processing, on the compressed sequence. It is clear that there is a strong interplay between the tools and techniques developed for SR and a number of other inverse problems encountered in signal processing (e.g., image restoration, motion estimation). SR techniques are being applied to a variety of fields, such as obtaining improved still images from video sequences (video printing), high definition television, high performance color Liquid Crystal Display (LCD) screens, improvement of the quality of color images taken by one CCD, video surveillance, remote sensing, and medical imaging. The authors believe that the SR/RE area has matured enough to develop a body of knowledge that can now start to provide useful and practical solutions to challenging real problems and that SR techniques can be an integral part of an image and video codec and can drive the development of new coder-decoders (codecs) and standards

Introduction -- Bayesian formulation of super-resolution imagereconstruction -- Low-resolution image formation models -- Motionestimation in superresolution -- Estimation of high-resolution images -- Bayesian inference models in super resolution -- Super resolution for compression -- Epilogue -- Bibliography -- Index -- Author biography

 

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