SIA OpenIR  > 光电信息技术研究室
Adversarial networks for scale feature-attention spectral image reconstruction from a single RGB
Liu PF(刘鹏飞)1,2,3,4,5; Zhao HC(赵怀慈)1,2,4,5
Department光电信息技术研究室
Source PublicationSENSORS
ISSN1424-8220
2020
Volume20Issue:8Pages:1-17
Indexed BySCI ; EI
EI Accession number20201808591191
WOS IDWOS:000533346400268
Contribution Rank1
Keywordhyperspectral imaging generative adversarial network attention mechanism feature pyramid boundary supervision
Abstract

Hyperspectral images reconstruction focuses on recovering the spectral information from a single RGBimage. In this paper, we propose two advanced Generative Adversarial Networks (GAN) for the heavily underconstrained inverse problem. We first propose scale attention pyramid UNet (SAPUNet), which uses U-Net with dilated convolution to extract features. We establish the feature pyramid inside the network and use the attention mechanism for feature selection. The superior performance of this model is due to the modern architecture and capturing of spatial semantics. To provide a more accurate solution, we propose another distinct architecture, named W-Net, that builds one more branch compared to U-Net to conduct boundary supervision. SAPUNet and scale attention pyramid WNet (SAPWNet) provide improvements on the Interdisciplinary Computational Vision Lab at Ben Gurion University (ICVL) datasetby 42% and 46.6%, and 45% and 50% in terms of root mean square error (RMSE) and relative RMSE,respectively. The experimental results demonstrate that our proposed models are more accurate than the state-of-the-art hyperspectral recovery methods.

Language英语
WOS SubjectChemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS Research AreaChemistry ; Engineering ; Instruments & Instrumentation
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26747
Collection光电信息技术研究室
Corresponding AuthorLiu PF(刘鹏飞)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China
5.The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China
Recommended Citation
GB/T 7714
Liu PF,Zhao HC. Adversarial networks for scale feature-attention spectral image reconstruction from a single RGB[J]. SENSORS,2020,20(8):1-17.
APA Liu PF,&Zhao HC.(2020).Adversarial networks for scale feature-attention spectral image reconstruction from a single RGB.SENSORS,20(8),1-17.
MLA Liu PF,et al."Adversarial networks for scale feature-attention spectral image reconstruction from a single RGB".SENSORS 20.8(2020):1-17.
Files in This Item:
File Name/Size DocType Version Access License
Adversarial networks(2066KB)期刊论文出版稿开放获取CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liu PF(刘鹏飞)]'s Articles
[Zhao HC(赵怀慈)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu PF(刘鹏飞)]'s Articles
[Zhao HC(赵怀慈)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu PF(刘鹏飞)]'s Articles
[Zhao HC(赵怀慈)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Adversarial networks for scale feature-attention spectral image reconstruction from a single RGB.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.