A new approach of joint noise and blur removal for multiframe super-resolution reconstruction

The reconstruction of a super-resolution (SR) image from multiple low-resolution (LR) frames has drawn a lot of research attention over the past years. The SR reconstruction is an inherent ill-posed minimization problem. Though regularization constraint may be accomplished to derive a single solution, to implement a traditional approach which the data fusing is processed at high-resolution (HR) grids may still be a great challenge due to complexity as well as high computing load. In this paper we introduce a new approach which both the blur and noise removal would be simultaneously carried out at the LR grids by taking pixelwise time average upon the available LR frames after registration. This approach is characterized by simplicity and real-time applicability, and the simulation results demonstrate that the compatible performance can be achieved.