A New Subspace Identification Approach Based on Principal Component Analysis and Noise Estimation

2015 ◽  
Vol 54 (18) ◽  
pp. 5106-5114 ◽  
Author(s):  
Ping Wu ◽  
HaiPeng Pan ◽  
Jia Ren ◽  
Chunjie Yang
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Noise Estimation ◽  
Subspace Identification ◽  
Identification Approach

scholarly journals Image Denoising Algorithm Combined with SGK Dictionary Learning and Principal Component Analysis Noise Estimation

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Wenjing Zhao ◽  
Yue Chi ◽  
Yatong Zhou ◽  
Cheng Zhang
Keyword(s):  
Principal Component Analysis ◽  
Standard Deviation ◽  
Dictionary Learning ◽  
Learning Algorithm ◽  
Principal Component ◽  
Estimation Algorithm ◽  
Component Analysis ◽  
Noise Estimation ◽  
Noise Standard Deviation ◽  
Estimation Algorithms

SGK (sequential generalization of K-means) dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA) noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental results show the following: (1) The SGK algorithm has the best denoising performance compared with the other three dictionary learning algorithms. (2) The SGK algorithm combined with PCA is superior to the SGK algorithm combined with other noise estimation algorithms. (3) Compared with the original SGK algorithm, the proposed algorithm has higher PSNR and better denoising performance.

Noise Estimation of Hyper-spectral Infrared Atmospheric Sounder Observations Using Principal Component Analysis

2019 ◽  
Vol 48 (2) ◽  
pp. 212001
Author(s):  
李路 LEE Lu ◽  
漆成莉 QI Cheng-li ◽  
张鹏 ZHANG Peng ◽  
胡秀清 HU Xiu-qing ◽  
顾明剑 GU Ming-jian
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Noise Estimation ◽  
Hyper Spectral
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