General observation model for an iterative multiframe regularized Super-Resolution Reconstruction for video enhancement

In general, the classical SRR algorithms are usually based on translational observation model hence these SRR algorithms can be applied only on the sequences that have simple translation motion. In order to cope with real video sequences and complex motion sequences, this paper proposes a general observation model for SRR algorithm, fast affine block-based transform, devoted to the case of nonisometric inter-frame motion. The proposed SRR algorithm is based on a maximum a posteriori estimation technique by minimizing a cost function. The classical L1 and L2 norm are used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image, removing outliers in the data and errors due to possibly inaccurate motion estimation. Tikhonov regularization is used as prior knowledge for removing outliers, resulting in sharp edges and forcing interpolation along the edges and not across them. The efficacy of the proposed algorithm is demonstrated here in a number of experimental results using standard video sequence such as Susie and Foreman at several noise power and models.