This paper introduces a novel end-to-end deep learning framework to learn space-time super-resolution (SR) process. We propose a coupled deep convolutional auto-encoder (CDCA) which learns the non-linear mapping between convolutional features of up-sampled low-resolution (LR) video sequence patches and convolutional features of high-resolution (HR) video sequence patches. The upsampling in LR video refers to tri-cubic interpolation both in space and time. We also propose a H.264/AVC compatible video space-time SR framework by using learned CDCA, which enables to super-resolve compressed LR video with less computational complexity. The experimental results prove that the proposed H.264/AVC compatible framework performs better than the state-of-art techniques on space-time SR in terms of quality and time complexity.
CITATION STYLE
Sharma, M., Chaudhury, S., & Lall, B. (2017). Space-Time Super-Resolution Using Deep Learning Based Framework. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10597 LNCS, pp. 582–590). Springer Verlag. https://doi.org/10.1007/978-3-319-69900-4_74
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