Bayesian Super-Resolution image reconstruction using an ℓ1 prior
This paper deals with the problem of high-resolution (HR) image reconstruction, from a set of degraded, under-sampled, shifted and rotated images, under the Bayesian paradigm, utilizing a variational approximation. Bayesian methods rely on image models that encapsulate prior image knowledge and avoid the ill-posedness of the image restoration problems. In this paper a new prior based on the lscr1 norm of vertical and horizontal first order differences of image pixel values is introduced and its parameters are estimated. The estimated HR images are compared with images provided by other HR reconstruction methods.