Thursday, April 06, 2017

CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing - implementation -

The Great Convergence continues: Compressive Sensing allowed the encoder to be non iterative and now a set of neural networks (CNNs, LSTMs) make the decoding also non iterative. Woohoo !

Fengbo who just received an interesting NSF award also sent me the following:

Dear Igor, 
Hope all is well.
Recently, we proposed a deep learning based encoding/decoding framework (CSVideoNet) for high-frame-rate video compressive sensing. We believe you and your blog’s readers would be interested in this research. The preview of the manuscript can be found here:

CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing by Kai Xu, Fengbo Ren
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 "CSVideoNet". 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-off between compression ratio (CR) and spatial-temporal resolution of the reconstructed videos. The experiment results demonstrate that CSVideoNet significantly 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 this https URL
Fengbo​ Ren
Director, Parallel Systems and Computing Laboratory (PSCLab)
​Assistant Professor, School of Computing, Informatics, and Decision Systems Engineering (CIDSE)
Arizona State University (ASU)

Thanks Fengbo !

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