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Document Type : Latin Dissertation
Language of Document : English
Record Number : 149529
Doc. No : ET21321
Main Entry : Sonal Ambwani
Title Proper : MAP BASED STOCHASTIC METHODS FOR JOINT ESTIMATION OF UNKNOWN IMAGE DEGRADATION PARAMETERS AND SUPER-RESOLUTION
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : In this thesis, two methods for the Maximum A Posteriori (MAP) based superresolutionare proposed. All the degradation parameters, namely, additive noise,blur and sub-pixel motion are considered unknown. The study focuses on the simultaneousestimation of the unknown parameters and the underlying high-resolutionimage. Two types of image priors have been considered, the Gaussian SimultaneousAutoregressive (SAR) and the Huber Markov Random Field (HMRF), and theresults have been compared. Special focus is laid on the estimation of the PSFblurring and two methods have been proposed for the modeling of PSF blur and itsestimation. Mathematical derivations and analytical proofs support the algorithms.Conclusions are drawn on the basis of the performance evaluation of the proposedalgorithms with each other and with the existing techniques. It is shown that thetwo methods proposed achieve stable and desired solution for the super-resolutionproblem..
Subject : Electericl tess
: برق
electronic file name : TL44478.pdf
Title and statement of responsibility and : MAP BASED STOCHASTIC METHODS FOR JOINT ESTIMATION OF UNKNOWN IMAGE DEGRADATION PARAMETERS AND SUPER-RESOLUTION [Thesis]
 
 
 
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