A Bayesian Super Resolution Algorithm Based on Synthetic Gradient Distribution

A novel Bayesian super resolution (SR) algorithm based on the distribution of synthetic gradient is proposed.The synthetic gradient combines prior information in horizontal,vertical,and diagonal directions.Its distribution is modeled as a Lorentzian function and regarded as a new image model which can sufficiently regularize the ill-posed algorithm and preserve the edges in the reconstructed images.The graduated nonconvexity (GNC)optimization is employed to guarantee the convergence of the proposed Lorentzian SR (LSR) algorithm to the global minimum.The performance of LSR is compared with conventional algorithms,and experimental results demonstrate that the proposed algorithm obtains both subjective and objective gains.