Robust Single-Image Super-Resolution using Cross-Scale Self-Similarity
Jordi Salvador (Technicolor)
Eduardo Pérez-Pellitero (Technicolor)
Axel Kochale (Technicolor)
Proceedings of the IEEE International Conference on Image Processing, 2014
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Abstract
We present a noise-aware single-image super-resolution (SISR) algorithm, which automatically cancels additive noise while adding detail learned from lower-resolution scales. In contrast with most SI-SR techniques, we do not assume the input image to be a clean source of examples. Instead, we adapt the recent and efficient in-place cross-scale self-similarity prior for both learning fine detail examples and reducing image noise. The experimental results show a promising performance, despite the relatively simple algorithm. Both objective evaluations and subjective validations show clear quality improvements when upscaling noisy images.
BibTeX
@inproceedings { Salvador2014,author = {Salvador, J. and P\'erez-Pellitero, E. and Kochale, A.},
title = {{Robust Single-Image Super-Resolution using Cross-Scale Self-Similarity}},
booktitle = {Proc. IEEE Int. Conf. on Image Processing},
pages = {first--last},
year = {2014},
}
