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oai:www.ideals.illinois.edu:2142/80794 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Moulin, Pierre Ishwar, Prakash 2015-09-25T20:08:11Z 2015-09-25T20:08:11Z 10000-01-01 2002 2002 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. Made available in DSpace on 2015-09-25T20:08:11Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3070334.pdf: 5999136 bytes, checksum: a23257e623fc33ade04c224fe23a5416 (MD5) Previous issue date: 2002 Embargo set by: Seth Robbins for item 82076 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 114 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2002. http://hdl.handle.net/2142/80794 (MiAaPQ)AAI3070334 eng Statistics A Unified Framework for Image Modeling and Estimation Using Measurement Constraints text Electrical Engineering Electrical Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.