Image super-resolution aims to recover a visually pleasing high resolution image from one or multiple low resolution images. It plays an essential role in a variety of real-world applications. In this paper, we propose a novel hybrid example-based single image superresolution approach which integrates learning from both external and internal exemplars. Given an input image, a proxy image with the same resolution as the target high-resolution image is first generated from a set of externally-learnt regression models. We then perform a coarse-to-fine gradient-level self-refinement on the proxy image guided by the input image. Finally, the refined high-resolution gradients are fed into a uniform energy function to recover the final output. Extensive experiments demonstrate that our framework outperforms the recent state-of-the-art single image super-resolution approaches both quantitatively and qualitatively.
CITATION STYLE
Xian, Y., Yang, X., & Tian, Y. (2015). Hybrid example-based single image super-resolution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9475, pp. 3–15). Springer Verlag. https://doi.org/10.1007/978-3-319-27863-6_1
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