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Institution:人工智能学院

Title of Paper:Visualizing and Understanding of Learned Compressive Sensing with Residual Network

Teaching and Research Group:智能工程系

Journal:NEUROCOMPUTING

Place of Publication:AMSTERDAM, NETHERLANDS

Project Source:国家自然基金

Key Words:Compressive sensing; Visualizing and understanding; Learned measurement matrix; Residual network

Summary:Recent years a variety of CNN-based (convolutional neural network) approaches for compressive sensing (CS) have been proposed. They learn a transform to recover the original signals from the measurements obtained by measuring the scene at a sub-Nyquist sampling rate. Among them, the LMM-based ones ( learned measurement matrix) exhibit better performance. In this paper, we visualize the LMM-based CS framework. This is the first time an insight look is taken into the CS network. It helps us understand how CS framework works......

All the Authors:Chenye Wang,Wan Liu,SHIGUANGMING,Jiang Du

First Author:ZHAOZHIFU

Indexed by:Journal paper

Correspondence Author:XIEXUEMEI

Document Code:000478960700017

Discipline:Engineering

First-Level Discipline:Electronics Science and Technology

Document Type:J

Volume:359

Page Number:185-198

ISSN:0925-2312

Translation or Not:No

Date of Publication:2019-09-24

Included Journals:SCI

Links to Published Journals:https://www.sciencedirect.com/science/article/pii/S0925231219307283?via%3Dihub

Date:2020-09-10