CSMCNet：具有可解释运动估计的可伸缩视频压缩感知重建
CSMCNet: Scalable Video Compressive Sensing Reconstruction with Interpretable Motion Estimation
摘要：
大多数用于压缩感知重建的深度网络方法都受到DNN的黑盒特性的影响。本文提出了一种具有可解释的运动估计的深度神经网络CSMCNet。该网络能够实现高质量的视频压缩感知重建展开的优化算法的迭代步骤。该方法设计了一个基于DNN的多假设运动估计模块来提高重建质量，并引入残差模块来进一步缩小重建结果与原始信号之间的差距。此外，我们提出了一个插值模块，并相应的训练策略，以实现可扩展的CS重建，这是能够使用相同的模型来解码不同的压缩比。实验结果表明，在压缩比为2%时（压缩率为98%），该方法的峰值信噪比达到29.34dB，优于其他现有方法。此外，插值模块被证明是有效的，具有显着的成本节省和可接受的性能损失。
英文摘要：
Most deep network methods for compressive sensing reconstruction suffer from the black-box characteristic of DNN. In this paper, a deep neural network with interpretable motion estimation named CSMCNet is proposed. The network is able to realize high-quality reconstruction of video compressive sensing by unfolding the iterative steps of optimization based algorithms. A DNN based, multi-hypothesis motion estimation module is designed to improve the reconstruction quality, and a residual module is employed to further narrow down the gap between re-construction results and original signal in our proposed method. Besides, we propose an interpolation module with corresponding training strategy to realize scalable CS reconstruction, which is capable of using the same model to decode various compression ratios. Experiments show that a PSNR of 29.34dB can be achieved at 2% CS ratio (compressed by 98%), which is superior than other state-of-the-art methods. Moreover, the interpolation module is proved to be effective, with significant cost saving and acceptable performance losses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
