10 papers with code • 0 benchmarks • 2 datasets
We show that by empowering the recurrent framework with the enhanced propagation and alignment, one can exploit spatiotemporal information across misaligned video frames more effectively.
We consider the problem of space-time super-resolution (ST-SR): increasing spatial resolution of video frames and simultaneously interpolating frames to increase the frame rate.
Recently, a number of data-driven frame interpolation methods based on convolutional neural networks have been proposed.
Ranked #10 on Video Frame Interpolation on Vimeo90k
In this work, we propose a motion estimation and motion compensation driven neural network for video frame interpolation.
Ranked #5 on Video Frame Interpolation on Middlebury
Finally, experiments validate the effectiveness and generalization ability of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video.
In addition to qualitative and quantitative evaluation of existing methods on publicly available and our proposed datasets, we also validate their performance in face detection in the dark.
Recent years have witnessed remarkable success of deep learning methods in quality enhancement for compressed video.
In our study, we analyze the proposed methods of the challenge and several methods in previous works on the proposed LDV dataset.