论文标题
CS-MCNET：具有可解释运动补偿的视频压缩感测重建网络
CS-MCNet:A Video Compressive Sensing Reconstruction Network with Interpretable Motion Compensation
论文作者
论文摘要
在本文中，提议具有可解释的运动补偿的深度神经网络，称为CS-MCNET，以实现视频压缩感测的高质量和实时解码。首先，在我们的网络中应用了明确的多肢体运动补偿来提取相邻帧的相关信息（如图1所示），从而改善了恢复性能。然后，残留模块进一步缩小了重建结果和原始信号之间的差距。通过使用算法展开可以解释整体体系结构，这带来了能够转移有关常规算法的先验知识的好处。结果，可以在64倍的压缩率下实现22dB的PSNR，比最先进的方法高约4％至9％。此外，由于馈电体系结构，我们的网络可以实时处理重建，并且比传统迭代方法更快地处理三个数量级。
In this paper, a deep neural network with interpretable motion compensation called CS-MCNet is proposed to realize high-quality and real-time decoding of video compressive sensing. Firstly, explicit multi-hypothesis motion compensation is applied in our network to extract correlation information of adjacent frames(as shown in Fig. 1), which improves the recover performance. And then, a residual module further narrows down the gap between reconstruction result and original signal. The overall architecture is interpretable by using algorithm unrolling, which brings the benefits of being able to transfer prior knowledge about the conventional algorithms. As a result, a PSNR of 22dB can be achieved at 64x compression ratio, which is about 4% to 9% better than state-of-the-art methods. In addition, due to the feed-forward architecture, the reconstruction can be processed by our network in real time and up to three orders of magnitude faster than traditional iterative methods.
