@article{YANG2022, 
author = {Chunling YANG and Xi LING and Zeyu LÜ},
title = {Feature-Domain Multi-Hypothesis Prediction Neural Network for Compressed Video Sensing Reconstruction},
year = {2022},
journal = {Journal of South China University of Technology (Natural Science Edition)},
volume = {50},
number = {6},
pages = {80-90},
keywords = {compressed sensing, deep learning, multi-hypothesis prediction, adaptive hypothesis weight, multi-frame reference reconstruction},
url = {https://www.sciopen.com/article/10.12141/j.issn.1000-565X.210507},
doi = {10.12141/j.issn.1000-565X.210507},
abstract = {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.}
}