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Efficient Video Super-Resolution through Recurrent Latent Space Propagation

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With the recent trend for ultra high definition displays, the demand for high quality and efficient video super-resolution (VSR) has become more important than ever. Previous methods adopt complex motion compensation strategies to exploit temporal information when estimating the missing high frequency details. However, as the motion estimation problem is a highly challenging problem, inaccurate motion compensation may affect the performance of VSR algorithms. Furthermore, the complex motion compensation module may also introduce a heavy computational burden, which limits the application of these methods in real systems. In this paper, we propose an efficient recurrent latent space propagation (RLSP) algorithm for fast VSR. RLSP introduces high-dimensional latent states to propagate temporal information between frames in an implicit manner. Our experimental results show that RLSP is a highly efficient and effective method to deal with the VSR problem. We outperform current state-of-the-art method DUF with over 70x speed-up.

Dario Fuoli, Shuhang Gu, Radu Timofte• 2019

Related benchmarks

TaskDatasetResultRank
Video Super-ResolutionVid4 (test)
PSNR27.51
173
Video Super-ResolutionREDS4 (test)
PSNR (Avg)30.47
117
Video Super-ResolutionVimeo-90K-T (test)
PSNR36.49
82
Video Super-ResolutionUDM10 (test)
PSNR38.5
51
Video Super-ResolutionVimeo-90K-T BI degradation (test)
PSNR36.49
47
Video Super-ResolutionVimeo-90K Medium (test)
PSNR (dB)36.97
39
Video Super-ResolutionVimeo-90K Fast (test)
PSNR (dB)39.2
39
Video Super-ResolutionVimeo-90K Slow (test)
PSNR (dB)33.86
39
Video Super-ResolutionSPMCS (test)
Avg. PSNR29.64
36
Video Super-ResolutionVimeo-90K-T 87 (test)
PSNR36.49
32
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