A Robust Video Super-Resolution Using a Recursive Leclerc Bayesian Approach with an OFOM (Optical Flow Observation Model)

Due to the inaccuracy of image registration between each frame of observed sequence, especially in a very complex motion frame, almost Video Super Resolution Reconstruction (SRR) frameworks found in review literatures cannot be worked well to real sequences with arbitrary scene content and/or arbitrary motion. Moreover, the observed system noise is typically assumed to be a Gaussian distribution thus the performance of SRR algorithm is usually degraded when the real system noise is non-Gaussian distribution. This paper proposes the alternative SRR framework for applying in the complex sequences and for against non-Gaussian noise models. First, the proposed SRR framework is based on classical stochastic ML (Minimization Likelihood) framework using L1, L2 and Leclerc norm estimations in order to measure the difference between the projected estimation of the reconstructed image and each observed images and to remove noise in the observed images. Later, the proposed algorithm is used an Optical Flow Observation Model (OFOM) based on 2D optical flow Block-Based Full (BOF) search algorithm for coping with complex motion between two frames of observed sequences. Finally, the experimental section shows that the proposed framework can be well effectively worked on real sequences such as Susie and Foreman sequences under several Gaussian and Non-Gaussian noise models (such as AWGN, Poisson, Salt & Pepper noise and Speckle) at different noise powers.