Total least square kernel regression

Authors:
Hiêp Luong;Bart Goossens;Aleksandra Piurica;Wilfried Philips
Affiliations:
Department of Telecommunications and Information Systems, IBBT, Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium;Department of Telecommunications and Information Systems, IBBT, Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium;Department of Telecommunications and Information Systems, IBBT, Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium;Department of Telecommunications and Information Systems, IBBT, Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium
Venue:
Journal of Visual Communication and Image Representation
Year:
2012

Citing 6
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Abstract

In this paper, we study the problem of robust image fusion in the context of multi-frame super-resolution. Given multiple aligned noisy low-resolution images, image fusion produces a new image on a high-resolution grid. Recently, kernel regression is presented as a powerful image fusion technique. However, in the presence of registration errors, the performance of kernel regression is quite poor. Therefore, we present a new kernel regression method that takes these registration errors into account. Instead of the ordinary least square metric, we employ the total least square metric, which allows for spatial perturbations of the image samples. We show in our experiments that our method is more robust to noise and/or registration errors compared to the traditional kernel regression algorithm.