A Unified Framework for Image Modeling and Estimation Using Measurement Constraints

A new, rich class of maxent priors for natural images from imprecise subband statistics in multiple orthonormal wavelet bases is developed. Experimental results for the problem of image restoration in additive white Gaussian noise are presented. Denoising and restoration of natural images using algorithms based on these maxent priors demonstrate significant improvements in terms of both perceptual quality as well as mean-squared error over classical approaches such as adaptive Wiener filtering. Under appropriate conditions, a variety of classical wavelet-domain image models and denoising algorithms are shown to be subsumed by the proposed multiple-domain maxent modeling and estimation framework.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Ishwar, Prakash

Contributors dc:contributor

• Moulin, Pierre

• Statistics

Language dc:language

eng

Identifier

(MiAaPQ)AAI3070334

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/80794