Super resolution combines a sequence of low-resolution noisy blurred images and produces either a higher resolution image or sequence. It can significantly increase image resolution without changing optical/mechanical/electrical imaging characteristics of the camera device. Existing restoration based super-resolution methods require enhancement factors (magnification) to be integer values. When the number of low resolution image frames is limited, the existing methods estimate spatial information from neighborhood so that the resulting high resolution image is blurred. Also, in real-time systems, a fixed object size for every image sequence frame is often desired. In such cases, resolution enhancement factors must be an arbitrary real number. In order to tackle these problems, we propose an alternate approach based upon a modified mathematics model and modified Maximum Likelihood (ML) estimator. Using our new model and modified ML, resolution enhancement factor can be any real number and traditional regularization operation of image restoration is not necessary. Therefore sharp edge and other high frequency contents are highly preserved. In this proposed method, L2 norm minimization is applied for data fusion without any regularization so that optimal and robust results are achieved and computation complexity is low. Also, in this paper an optimal "enhancement factor" algorithm is proposed.

Date Published: 27 April 2009
PDF: 11 pages
Proc. SPIE 7341, Visual Information Processing XVIII, 73410H (27 April 2009); doi: 10.1117/12.819381

Published in SPIE Proceedings Vol. 7341:
Visual Information Processing XVIII
Zia-Ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)