Feature-Domain Multi-Hypothesis Prediction Neural Network for Compressed Video Sensing Reconstruction

In the prediction-residual reconstruction framework, multi-hypothesis prediction through video temporal correlation is a key step in video compressed sensing reconstruction. Aiming at the problems that the current video compressive sensing multi-hypothesis reconstruction neural network has insufficient prediction accuracy and poor theoretical explanation, this paper proposed a feature-domain multi-hypothesis prediction video compressive sensing reconstruction network (FMH_CVSNet) based on the traditional multi-hypothesis theory. Firstly, a new feature-domain multi-hypothesis prediction module was proposed to enhance the prediction ability of the network by constructing a reasonable motion estimation module and hypothesis weight solving module. Then, a two-stage multi-refe-rence frame motion compensation mode was proposed to adapt the sequence features to construct a better hypothesis set to further improve the prediction accuracy. The simulation results show that FMH_CVSNet achieves better reconstruction performance under all experimental conditions, and the average PSNR is improved by 4.76 and 3.87 dB, respectively, compared with 2 sMHR and VCSNet-2.