Multiframe resolution-enhancement using a robust iterative SRR based on leclerc stochastic technique

dc.contributor.author Vorapoj Patanavijit
dc.date.accessioned 2016-06-13T03:39:01Z
dc.date.available 2016-06-13T03:39:01Z
dc.date.issued 2009-10
dc.description.abstract This paper proposes a multiframe resolution-enhancement using a robust iterative SRR (Super-Resolution Reconstruction) for applying on images that is corrupted by several nose models. Typically, the success of SRR algorithms is highly dependent on the model accuracy regarding the imaging process. The real noise models corrupting the measure sequence are unknown hence SRR algorithms using L1 or L2 norm may degrade the image sequence rather than enhance it. The proposed enhancement algorithm is based on the stochastic regularization SRR technique of Bayesian MAP estimation by minimizing a cost function. The Leclerc norm is used for removing outliers in the data and for measuring the difference between the projected estimate of the HR image and each LR image. Due to the ill-pose problem, Tikhonov regularization is used to remove artifacts from the final answer and improve the rate of convergence. The experimental results show the effectiveness of our methods and demonstrate its superiority to other SRR algorithm based on L1 and L2 norm for several noise models such as Noiseless, AWGN, Poisson Noise, Salt&Pepper Noise and Speckle Noise. en_US
dc.format.extent 4 pages en_US
dc.format.mimetype application/pdf en_US
dc.identifier.citation Proceedings of The 32nd Electrical Engineering Conference (EECON-32), Prachinburi, Thailand, (October 28-30, 2009) en_US
dc.identifier.uri https://repository.au.edu/handle/6623004553/17925
dc.language.iso eng en_US
dc.subject Image reconstruction en_US
dc.subject Image enhancement en_US
dc.subject Video signal processing en_US
dc.title Multiframe resolution-enhancement using a robust iterative SRR based on leclerc stochastic technique en_US
dc.type Text en_US
mods.genre Proceeding Paper en_US
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