Selective Bayesian estimation for efficient super-resolution
In this paper, a new approach to efficient and robust super-resolution is presented. Our method is based on selectively applying a Bayesian MAP estimator to image regions with high spatial activity. The degree of spatial activity is measured using the gradient of the estimated high-resolution image at each iteration. In addition, selective filtering is applied to enhance the visual quality of the estimated high-resolution image. The results obtained via simulation and with real video sequences demonstrate up to a 50% reduction in computational complexity, with improved visual quality, and higher SNR gains for magnification factors of four or more.