A non-stationary image prior combination in super-resolution |
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| Affiliation: | 1. Dept. de Lenguajes y Sistemas Informáticos, Universidad de Granada, 18071 Granada, Spain;2. Dept. de Ciencias de la Computación e I. A., Universidad de Granada, 18071 Granada, Spain;3. Dept. of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208-3118, United States;1. Department of Physics, Swansea University, Singleton Park, Swansea, SA2 8PP, UK;2. Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15260, USA;1. Department of Mathematical Sciences, University of Delaware, Newark DE 19716, United States;2. Facultad de Matemáticas, Pontificia Universidad Católica de Chile, Avenida Vicuña Mackenna 4860, Santiago, Chile;1. Harvard School of Engineering and Applied Sciences, Cambridge, MA 02138, USA;2. 82 Powers Road, Concord, MA 01742, USA |
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| Abstract: | A new Bayesian Super-Resolution (SR) image registration and reconstruction method is proposed. The new method utilizes a prior distribution based on a general combination of spatially adaptive, or non-stationary, image filters, which includes an adaptive local strength parameter able to preserve both image edges and textures. With the application of variational techniques, the proposed method allows for the automatic estimation of all problem unknowns. An experimental comparison between state of the art methods and the proposed SR approach has been performed on both synthetic and real images. |
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| Keywords: | Super-resolution Total variation Variational methods Parameter estimation Bayesian methods |
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