基于MAP的多信息流梯度更新与聚合视频压缩感知重构算法OACSCDCSTPCD

Due to the lack of guidance from the parallel update theoretical solver framework,existing deep learning-based distributed compressed video sensing(DCVS)algorithms alternately use measurement values and reference frames to optimize the reconstructed non-key frame,resulting in the inability to fully combine the two types of information and limit-ing the quality of reconstruction.In order to solve this problem,this paper firstly uses Bayesian theory and maximum a pos-teriori estimation(MAP)to derive the optimization equation of non-key frame reconstruction in DCVS,and then derives the solution framework of the optimization equation based on the proximal gradient algorithm,including multi-information flow gradient update and aggregation equation.Based on it,this paper designs a multi-information flow gradient update and aggregation neural network module(MIGA),and constructs a deep multi-information flow gradient update and aggregation network(DMIGAN)for DCVS non-key frame reconstruction.MIGA uses the measurement values and multiple reference frames to update the current non-key frame by parallel gradients,and then performs information interaction and fusion,so as to fully combine multiple information flows to update and reconstruct the frame.In this paper,the MIGA and the denois-ing sub-network are cascaded to simulate a single iteration of the proximal gradient algorithm as the basic phase.The deep reconstruction network DMIGAN is constructed by cascading multiple phases to realize the deep optimization process of frame reconstruction.Experiments show that,compared with the representative traditional iterative optimization algorithm structural similarity based inter-frame group sparse representation(SSIM-InterF-GSR),the performance of DMIGAN is im-proved by 8.8 dB and 7.36 dB at low sampling rate and high sampling rate respectively;and compared with the representa-tive deep learning reconstruction algorithm VCSNet-2,the performance is improved by 7.09 dB and 8.78 dB at low sam-pling rate and high sampling rate,respectively.