scholarly journals Image Informative Maps for Estimating Noise Standard Deviation and Texture Parameters

2011 ◽  
Vol 2011 (1) ◽  
Author(s):  
M. Uss ◽  
B. Vozel ◽  
V. Lukin ◽  
S. Abramov ◽  
I. Baryshev ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Noise Standard Deviation ◽  
Texture Parameters

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.

Ionosphere Propagation Error Correction for Galileo

2003 ◽  
Vol 56 (1) ◽  
pp. 45-50 ◽  
Author(s):  
P. D. Groves ◽  
S. J. Harding
Keyword(s):  
Standard Deviation ◽  
Theoretical Analysis ◽  
Error Correction ◽  
Service User ◽  
Smoothing Algorithm ◽  
Dual Frequency ◽  
Noise Standard Deviation ◽  
Range Measurements ◽  
Code Tracking

A theoretical analysis of the effects of dual frequency ionosphere propagation correction on GNSS code tracking noise is presented. The effect on tracking noise of using different combinations of the proposed Galileo signals for ionosphere propagation correction is investigated. It is concluded that, for the open or commercial service user, the Galileo signals E2-L1-E1 near to and around GPS L1 should be used to apply ionosphere propagation corrections to the E5 signals. This produces a tracking noise standard deviation about three times larger than that on an E5 signal alone. An ionosphere correction-smoothing algorithm is presented that reduces the tracking noise on the corrected pseudo-range measurements.

Design of Virtual Function Signal Generator Based on Sound Card

2014 ◽  
Vol 543-547 ◽  
pp. 850-853
Author(s):  
Hui Ling Si
Keyword(s):  
Standard Deviation ◽  
White Noise ◽  
Instrument Development ◽  
Virtual Instrument ◽  
Sine Wave ◽  
Signal Generator ◽  
Sound Card ◽  
Noise Standard Deviation ◽  
Development Platform ◽  
The Cost

In this paper, the use of Lab VIEW virtual instrument development platform, designs the virtual function signal generator based on sound card. The instrument can generate sine wave, square wave, saw tooth wave, triangle wave, gauss white noise, superposition of sine wave, custom formula waveform, it can be arbitrarily set parameter as frequency, amplitude, phase, the noise standard deviation, it has simple operation, good interaction, it save the cost and can be widely used in scientific research and experiment teaching.

scholarly journals Detection of Climate Signal by Inter-stake Correlations of Annual Ablation Data Qamanârssûp Sermia, West Greenland

1989 ◽  
Vol 35 (120) ◽  
pp. 253-259 ◽  
Author(s):  
Roger J. Braithwaite ◽  
Ole В Olesen
Keyword(s):  
Standard Deviation ◽  
Greenhouse Effect ◽  
Time Variation ◽  
Correlation Coefficients ◽  
Annual Precipitation ◽  
Climate Information ◽  
Climate Signal ◽  
Noise Standard Deviation ◽  
West Greenland ◽  
Local Topography

Abstract Annual ablation on Qamanârssûp sermia, West Greenland, varies with year and location. The time variation of ablation at any stake consists of climate signal and noise due to errors and local topography. Variations of ablation at different stakes are positively correlated with each other with inter-stake correlation coefficients depending on the relative magnitudes of climate signal and noise. Inter-stake correlations and noise are very sensitive to gross errors in the data, while climate signal is less sensitive. Low values of inter-stake correlations are used to detect suspect data, and elimination of such data reduces the noise standard deviation from ±0.40 to ±0.28 m water a−1 for 6 years of record compared with a climate signal of ±0.55 m water a−1. The climate signal on Qamanârssûp is positively correlated with summer mean temperature and negatively correlated with annual precipitation. This shows that even a sparse stake network as at Qamanârssûp sermia gives useful glacier-climate information which can be applied to studies of both glacier hydrology and greenhouse effect.

ESTIMATION OF FRAME SEQUENCE NOISE WITH REMOVAL OF JPEG ARTIFACTS

2013 ◽  
Vol 13 (01) ◽  
pp. 1350003
Author(s):  
VASILEIOS I. ANAGNOSTOPOULOS ◽  
EMMANUEL S. SARDIS ◽  
THEODORA A. VARVARIGOU
Keyword(s):  
Computer Vision ◽  
Standard Deviation ◽  
Channel Correlation ◽  
Mathematical Basis ◽  
Noise Standard Deviation ◽  
Online System ◽  
Periodic Noise ◽  
Frame Sequence ◽  
Stochastic Linearization ◽  
Network Camera

This paper proposes a method to remove JPEG noise artifacts from frame sequences. Using extensive experimental results we show how an online system with periodic noise estimation functionality can estimate the real frame noise even if the images are in JPEG format. We present the mathematical basis of the methodology and show in real content that we can have reliable measurements. We also present the results obtained on a real network camera and show that our method can provide a much better estimation of the noise standard deviation compared to common practice but comparable inter-channel and spatial intra-channel correlation estimates. We also provide some guidelines for capturing datasets necessary to apply computer vision tasks. Our approach exploits the well known stochastic linearization phenomenon which we prove that is present in our case.

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