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

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 Geman&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.