The Performance Analysis of Universal Inspection Registration for Video Multi-Frame Super Resolution Reconstruction Established on Stochastic Maximum A Posteriori Framework
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Abstract
Traditionally, the classical Multi-frame Super Resolution Reconstruction (MSRR) schemes can be effectively implemented on the video with a simple shifting motion pattern because the conventional inspected model in video MSRR scheme is established on an ordinary registration. In this paper, the universal inspection registration (established on a fast affine block-based transform) has been proposed for handling with any real and complex inter-frame motion patterns therefore this the video MSRR cooperated with the proposed registration can be applied on any real and complex videos. Later, this paper mathematically presents the solution (or the refined SR image) of the video MSRR with the proposed registration under the regularization framework by using an optimized nonlinear programing technique. Using two tested video sequences such as Susie and Foreman with four noise models at several noise energy, the refined SR image products from experiments expose that the MSRR schemes with the proposed registration outperforms than the antecedent MSRR schemes with the an ordinary registration.
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