@inproceedings {270,
	title = {CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing},
	year = {2018},
	month = {03/2018},
	abstract = {<p>This paper addresses the real-time encoding-decoding problem for high-frame-rate video compressive sensing (CS). Unlike prior works that perform reconstruction using iterative optimization-based approaches, we propose a non-iterative model, named \&ldquo;CSVideoNet\&rdquo;. CSVideoNet directly learns the inverse mapping of CS and reconstructs the original input in a single forward propagation. To overcome the limitations of existing CS cameras, we propose a multi-rate CNN and a synthesizing RNN to improve the trade-o between compression ratio (CR) and spatial-temporal resolution of the reconstructed videos. The experiment results demonstrate that CSVideoNet\&nbsp;significantly\&nbsp;outperforms the state-of-the-art approaches. With no pre/post-processing, we achieve 25dB PSNR recovery quality at 100x CR, with a frame rate of 125 fps on a Titan X GPU. Due to the feedforward and high-data-concurrency natures of CSVideoNet, it can take advantage of GPU acceleration to achieve three orders of magnitude speed-up over conventional iterative-based approaches. We share the source code at https://github.com/PSCLab-ASU/CSVideoNet.</p>
},
	keywords = {psclab},
	doi = {10.1109/WACV.2018.00187},
	author = {Kai Xu and Fengbo Ren}
}
