Fast single-image super-resolution with filter selection
Jordi Salvador (Technicolor)
Eduardo Pérez-Pellitero (Technicolor)
Axel Kochale (Technicolor)
Proceedings of the IEEE International Conference on Image Processing 2013
For detailed results, please check the supplementary material.
Abstract
This paper presents a new method for estimating a super-resolved version of an observed image
by exploiting cross-scale self-similarity. We extend prior work on single-image super-resolution by introducing an adaptive selection of the best fitting upscaling and analysis filters for example learning. This selection is based on local error measurements obtained by using each filter with every image patch, and contrasts with the common approach of a constant metric in both dictionary-based and internal learning super-resolution. The proposed method is suitable for interactive applications, offering low computational load and a parallelizable design that allows straight-forward GPU implementations. Experimental results also show how our method generalizes better to different datasets than dictionary-based super-resolution and comparably to internal learning with adaptive post-processing.
BibTeX
@inproceedings {Salvador2013c,author = {Salvador, J. and Pérez-Pellitero, E. and Kochale, A.},
title = {Fast single-image super-resolution with filter selection},
booktitle = {Proc. IEEE Int. Conf. on Image Processing},
year = {2013},
}
