Abstract
Methods based on convolutional neural network (CNN) have demonstrated tremendous improvements on single image super-resolution. However, the previous methods mainly restore images from one single area in the low-resolution (LR) input, which limits the flexibility of models to infer various scales of details for high-resolution (HR) output. Moreover, most of them train a specific model for each up-scale factor. In this paper, we propose a multi-scale super resolution (MSSR) network. Our network consists of multi-scale paths to make the HR inference, which can learn to synthesize features from different scales. This property helps reconstruct various kinds of regions in HR images. In addition, only one single model is needed for multiple up-scale factors, which is more efficient without loss of restoration quality. Experiments on four public datasets demonstrate that the proposed method achieved state-of-the-art performance with fast speed.
This work is supported by the National Natural Science Foundation of China (61171142, 61401163, U1636218), the Science and technology Planning Project of Guangdong Province of China (2014B010111003, 2014B010111006), Guangzhou Key Lab of Body Data Science (201605030011).
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Jia, X., Xu, X., Cai, B., Guo, K. (2018). Single Image Super-Resolution Using Multi-scale Convolutional Neural Network. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_15
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