Abstract
In this paper, we propose an efficient and high-quality super-resolution method by combining Cross Classification Trees (CCTs)-based sample classification rule and Deep Forest mapping relationship. Firstly, we use a number of mapping kernels to divide sample space into overlapping subspaces and use decision trees to realize the classification function. In this process, the CCTs are used to classify samples, which can remarkably improve the coverage rate of the sample subspaces and increase the classification accuracy. Then, for each leaf node of CCTs, a Cascade Network (CN) is learned, which simultaneously uses priori and posteriori information to map low-resolution (LR) image patches to high-resolution (HR) image patches for each subspace. This is an ensemble approach in a data-dependent way with a larger sample and fewer hyper-parameters. Extensive experiment results demonstrate that the proposed method achieves better performance and costs less time.
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This work was supported in part by the National Natural Science Foundation of China (61701036).
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Lv, Y., Wang, Z., Duan, P., Kang, X. (2019). A Novel Image Super-Resolution Method Based on Cross Classification Trees and Cascaded Network. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_52
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DOI: https://doi.org/10.1007/978-981-13-9917-6_52
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