Super-resolution reconstruction of an image
Michael Elad, Arie Feuer · 2002
This paper presents a generalization of restoration theory for the problem of super-resolution reconstruction (SRR) of an image. In the SRR problem, a set of low quality images is given, and a single improved quality image which fuses their information is required. We present a model for this problem, and show how the classic restoration theory tools-maximum likelihood estimator (ML), maximum a posteriori probability estimator (MAP) and the projection onto convex sets (POCS)-can be applied as a solution. A hybrid algorithm which joins the POCS and the ML benefits is suggested.