在预测-残差重构框架中，利用视频时间相关性进行多假设预测是视频压缩感知重构的关键步骤。本文基于深度学习，研究了利用丰富的特征域信息实现更精确预测的方法，提出一种特征域多假设预测视频压缩感知重构网络(FMH_CVSNet)。首先提出一种新的特征域多假设预测模块（FMH_Module），通过构造合理的运动估计模块与假设权重求解模块增强了网络的预测能力；其次提出两阶段多参考帧运动补偿模式，使不同运动特征序列均能构造更优假设集，进一步提升了预测精度。仿真结果表明，FMH_CVSNet在各实验条件下均取得了优秀的重构性能，相比于传统多假设算法2sMHR平均PSNR提升了4.76dB，相比于基于深度学习的视频压缩感知重构算法VCSNet-2提升了3.87dB。

In the prediction-residual reconstruction framework, multi-hypothesis prediction based on temporal correlation is the key step of compressed video sensing reconstruction. This paper studies the accuracy prediction method by utilizing rich features based on deep learning, and a novel feature-domain multi-hypothesis reconstruction network for compressed video sensing (FMH_CVSNet) is proposed. In FMH_CVSNet, the feature domain multi-hypothesis prediction module (FMH_Module) is firstly proposed, which improves the prediction ability by reasonably constructing the motion estimation module and the hypothesis weight calculation module based on the characteristics of video signal. Secondly, the two-stage multi-reference motion compensation mode is proposed, which makes the constructed hypothesis sets much better for sequences with different motion and the further improves the prediction accuracy. Simulation results show that FMH_CVSNet achieves better reconstruction performance under various experimental conditions, improves by 4.76dB compared with the traditional multi-hypothesis algorithm 2sMHR and improves by 3.87dB compared with CNN based compressed video sensing reconstruction algorithm VCSNet-2.