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Institution:人工智能学院

Title of Paper:A Hybrid-3D Convolutional Network for Video Compressive Sensing

Teaching and Research Group:智能工程系

Journal:IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Place of Publication:NJ 08855-4141 USA

Project Source:国家自然基金

Key Words:Hybrid-3D convolution; real time; video compressive sensing

Summary:Video Compressive Sensing (VCS) works to recover the scene video from limited compressed measurements. VCS was intended to sense and recover the scene video in spatial-temporal sensing manner. It is difficult to be performed due to the complexity of design and optimization. The most current approaches measure the scene video only in the spatial or temporal domain. However, this would lose the spatial-temporal correlation in VCS. Focus on this issue, this paper proposes a VCS framework, which uses the learned spatial-temporal sensing manner and the hybrid-3D recovery network......

All the Authors:WAN LIU,QINGZHE PAN

First Author:ZHAOZHIFU

Indexed by:Journal paper

Correspondence Author:XIEXUEMEI

Document Code:000525390100058

Discipline:Engineering

First-Level Discipline:Electronics Science and Technology

Document Type:J

Volume:8

Page Number:20503-20513

ISSN:2169-3536

Translation or Not:No

Date of Publication:2020-02-10

Included Journals:SCI

Links to Published Journals:https://ieeexplore.ieee.org/ielx7/6287639/8948470/08968334.pdf

Date:2020-09-10