Image super-resolution with B-Spline kernels

A novel approach to image super-resolution is described in this paper. By modeling our image acquisition system with a Spline sampling kernel, we are able to retrieve from the samples some statistical information about the observed continuous scene before its acquisition (irradiance light-field). This information, called continuous moments, allows to register exactly a set of low-resolution images and to ultimately generate a superresolved image. The novelty of the proposed algorithm resides in its ability to operate entirely on low-resolution images and to enhance the resolution of the entire field of view with a relatively low computational complexity. We ran experiments with real acquired images and obtained super-resolved images with a good level of details although some ringing effects are also noticeable.

Published in:
Proceedings of the 7th IMA International Conference on Mathematics in Signal Processing
Presented at:
IMA International Conference on Mathematics in Signal Processing, Cirencester, December, 2006

 Record created 2012-10-25, last modified 2018-01-28

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