Browsing by Subject "Video signal processing"
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ItemMultiframe resolution-enhancement using a robust iterative SRR based on leclerc stochastic technique( 2009-10) Vorapoj PatanavijitThis 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.
ItemVideo enhancement using a robust iterative SRR based on a German&McClure stochastic estimation with a general observation model( 2010-05) Vorapoj PatanavijitThis 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.