Video enhancement using a robust iterative SRR based on a German&McClure stochastic estimation with a general observation model

dc.contributor.author Vorapoj Patanavijit
dc.date.accessioned 2016-06-13T06:34:58Z
dc.date.available 2016-06-13T06:34:58Z
dc.date.issued 2010-05
dc.description.abstract This paper proposes the novel robust SRR algorithm that can be effectively applied on the sequence that are corrupted by various noise models and can be applied on the real or standard sequence. First, the proposed SRR algorithm is based on the German&McClure norm that used for measuring the difference between the projected estimate of the high quality image and each low high quality image and for removing outliers in the data. Second, in order to cope with real video sequences and complex motion sequences, the proposed SRR is based on a general observation model for SRR algorithm, fast affine block-based transform, devoted to the case of nonisometric inter-frame motion. The experimental results show that the proposed reconstruction can be efficiently applied on real sequences such as Suzie and Foreman sequence and confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods based on L1 and L2 norm for several noise models (such as AWGN, Poisson, Salt & Pepper noise and Speckle) and several noise power. en_US
dc.format.extent 5 pages en_US
dc.format.mimetype application/pdf en_US
dc.identifier.citation Proceedings of 2010 International Conference of Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2010), ECTI Association Thailand, Chiang Mai, Thailand, (May 19-21, 2010), 875-879 (IEEE Xplore) en_US
dc.identifier.uri https://repository.au.edu/handle/6623004553/17926
dc.language.iso eng en_US
dc.subject Image reconstruction en_US
dc.subject Video signal processing en_US
dc.subject Image enhancement en_US
dc.title Video enhancement using a robust iterative SRR based on a German&McClure stochastic estimation with a general observation model en_US
dc.type Text en_US
mods.genre Proceeding Paper en_US
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