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DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing

You can find the paper here.

In this paper, we propose a novel encoder-decoder neural network model called DeepBinaryMask for video compressive
sensing. The proposed framework is an end-to-end model where the sensing matrix is trained along with the video reconstruction. The
encoder learns the binary elements of the sensing matrix and the decoder is trained to reconstruct the video sequence.

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Video Super-Resolution with Convolutional Neural Networks

Armin Kappeler, Seunghwan Yoo, Qiqin Dai, Aggelos K. Katsaggelos

Abstract:

Video Super-Resolution with Convolutional Neural Networks

Convolutional neural networks (CNN) have so far been successfully applied to image super-resolution (SR) as well as other image restoration tasks. In this project, we consider the problem of video super-resolution. We propose a CNN that is trained on both the spatial and the temporal dimensions of videos to enhance their spatial resolution and show extensive comparison to the state-of-the-art video and image super-resolution algorithms.

The software can be found under the project page:
Video Super-Resolution with Convolutional Neural Networks

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