Super-resolution reconstruction of continuous image sequences

Super-resolution reconstruction algorithms perform a fusion of several low quality images of the same scene into a single improved quality image. As opposed to this STATIC recovery problem, in this paper we define a DYNAMIC super-resolution task: the restoration of a blurred, decimated, and noisy image sequence. We first model this problem through state-space equations, showing that this problem can be viewed as a sequence of STATIC super-resolution problems. Two efficient reconstruction algorithms are proposed, both being adaptive filtering approximations of the Kalman filter; the R-SD and the R-LMS. Computer simulations on synthetic sequences indicate the computational feasibility of these algorithms