An improved regularization method for artifact rejection in image super-resolution

The main aim of this paper is to employ an improved regularization method to super-resolution problems. Super-resolution refers to a process that increases spatial resolution by fusing information from a sequence of images acquired in one or more of several possible ways. This process is an inverse problem, one that is known to be highly ill-conditioned. Total Variation regularization is one of the well-known techniques used to deal with such problems, which has some disadvantages like staircase effect artifacts and nonphysical dissipation. To enhance the robustness of processing against these artifacts, this paper proposes a new regularization method based on the coupling of fourth order PDE and a type of newly designed shock filtering based on complex diffusion in addition to previous Total Variation. In order to have sharp corner structures like edges, this work also considers a corner shock filter. The proposed scheme is not only able to remove the jittering effect artifacts along the edge directions but also able to restrain the rounding artifacts around the corner structures and most importantly, the stabilization of the overall process is assured.