Compressed Video Sensing Based on Deep Generative Adversarial Network

This paper considers the deep-learning-aided compressed video sensing problem. To this end, a deep generative adversarial network has been proposed to provide an approximation of the non-reference frame using its corresponding reference frame. The tests confirm the superiority of this scheme over the conventional methods used earlier. Furthermore, two scenarios have been suggested for deep compressed video sensing and recovery. In the first scenario, the difference between the non-reference frame and its approximation obtained from the pre-trained network is compressively sampled and transmitted to the receiver where the proposed residual reconstruction network is adopted to reconstruct the signal. The second scenario utilizes a pre-trained network followed by an augmented layer to approximate the non-reference frames. In the transmitter, the parameters of the augmented layer are trained for the current non-reference block. Instead of transmitting the samples of the block, the parameters of its trained augmented layer are sent to the receiver where the reconstruction is done using the same pre-trained network. The performances of the proposed scenarios demonstrate their objective and subjective superiority over the state-of-the-art algorithms in both the reconstruction quality and run time.