Adaptive Reconstruction for Distributed Compressive Video Sensing Based on Texture Features
Distributed compressive video sensing(DCVS),possessing hardware-friendly characteristics,has emerged as a solution to the challenges posed by large-scale video data.Since traditional analytical model-based reconstruction methods for DCVS are computational-ly complex and hard to meet the requirements of real-time applications,deep learning technologies are gradually applied in DCVS.How-ever,the existing deep learning-based reconstruction methods ignore texture characterizes of frames,which limits reconstruction perform-ance.Based on the observation that frames within one group are highly correlated,the adjacent frames can be selected as the references to exploit texture features of current frames.To address this problem,a texture features-based adaptive reconstruction network for DCVS is proposed,dubbed'TF-DCVSNet'.Specifically,TF-DCVSNet utilizes the texture features of the reconstructed adjacent frames to acti-vate the reconstruction network module of the current reconstructed frame to perform adaptive reconstruction.Extensive experiments demonstrate the effectiveness of TF-DCVSNet.
distributed compressive video sensingvideo reconstructiondeep learningtexture features
万方数据
维普
