Abstract
Super-resolution algorithms are used to improve the quality and resolution of low-resolution images. These algorithms can be divided into two classes of hallucination- and reconstruction-based ones. The improvement factors of these algorithms are limited, however, previous research [9, 10] has shown that combining super-resolution algorithms from these two different groups can push the improvement factor further. We have shown in this paper that combining super-resolution algorithms of the same class can also push the improvement factor up. For this purpose, we have combined two hallucination based algorithms, namely the one found in Single Image Super-Resolution from Transformed Self-Exemplars [7] and the Super-Resolution Convolutional Neural Network from [4]. The combination of these two, through an alpha-blending, has resulted in a system that outperforms state-of-the-art super-resolution algorithms on public benchmark datasets.
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Aakerberg, A., Rasmussen, C.B., Nasrollahi, K., Moeslund, T.B. (2017). Complementing SRCNN by Transformed Self-Exemplars. In: Nasrollahi, K., et al. Video Analytics. Face and Facial Expression Recognition and Audience Measurement. VAAM FFER 2016 2016. Lecture Notes in Computer Science(), vol 10165. Springer, Cham. https://doi.org/10.1007/978-3-319-56687-0_11
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DOI: https://doi.org/10.1007/978-3-319-56687-0_11
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