Hyperspectral Image Denoising Via Texture-Preserved Total Variation Regularizer-Reference-Cited by-同舟云学术

Hyperspectral Image Denoising Via Texture-Preserved Total Variation Regularizer

Published:2023 Issue: Volume:61 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Chen Yang¹^ORCID,Cao Wenfei¹^ORCID,Pang Li²,Peng Jiangjun³^ORCID,Cao Xiangyong²^ORCID

Affiliation:

1. School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, China

2. School of Computer Science and Technology and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, China

3. School of Mathematics and Statistics and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10173585.pdf?arnumber=10173585

Reference55 articles.

1. Hyperspectral Image Classification With Convolutional Neural Network and Active Learning

2. Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network

3. Hyperspectral Image Denoising Employing a Spatial–Spectral Deep Residual Convolutional Neural Network

4. Sparse Representation for Target Detection in Hyperspectral Imagery

5. Scope of validity of PSNR in image/video quality assessment

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