scholarly journals Noise Removal Based on Tensor Modelling for Hyperspectral Image Classification

2018 ◽  
Vol 10 (9) ◽  
pp. 1330 ◽  
Author(s):  
Salah Bourennane ◽  
Caroline Fossati ◽  
Tao Lin
Keyword(s):  
White Noise ◽  
Spatial Resolution ◽  
Thermal Noise ◽  
Hyperspectral Image ◽  
Additive White Gaussian Noise ◽  
Wiener Filter ◽  
Noise Removal ◽  
Parameters Estimation ◽  
Photon Noise ◽  
Noise Parameters

With the current state-of-the-art computer aided manufacturing tools, the spatial resolution of hyperspectral sensors is becoming increasingly higher thus making it easy to obtain much more detailed information of the scene captured. However, the improvement of the spatial resolution also brings new challenging problems to address with signal dependent photon noise being one of them. Unlike the signal independent thermal noise, the variance of photon noise is dependent on the signal, therefore many denoising methods developed for the stationary noise cannot be applied directly to the photon noise. To make things worse, both photon and thermal noise coexist in the captured hyperspectral image (HSI), thus making it more difficult to whiten noise. In this paper, we propose a new denoising framework to cope with signal dependent nonwhite noise (SDNW), Pre-estimate—Whitening—Post-estimate (PWP) loop, to reduce both photon and thermal noise in HSI. Previously, we proposed a method based on multidimensional wavelet packet transform and multi-way Wiener filter which performs both white noise and spectral dimensionality reduction, referred to as MWPT-MWF, which was restricted to white noise. We get inspired from this MWPT-MWF to develop a new iterative method for reducing photon and thermal noise. Firstly, the hyperspectral noise parameters estimation (HYNPE) algorithm is used to estimate the noise parameters, the SD noise is converted to an additive white Gaussian noise by pre-whitening procedure and then the whitened HSI is denoised by the proposed method SDNW-MWPT-MWF. As comparative experiments, the Multiple Linear Regression (MLR) based denoising method and tensor-based Multiway Wiener Filter (MWF) are also used in the denoising framework. An HSI captured by Reflective Optics System Imaging Spectrometer (ROSIS) is used in the experiments and the denoising performances are assessed from various aspects: the noise whitening performance, the Signal-to-Noise Ratio (SNR), and the classification performance. The results on the real-world airborne hyperspectral image HYDICE (Hyperspectral Digital Imagery Collection Experiment) are also presented and analyzed. These experiments show that it is worth taking into account noise signal-dependency hypothesis for processing HYDICE and ROSIS HSIs.

A Wavelet Spatial Correlation Algorithm to Partial Discharge Denoising Based on MDL Criterion

2013 ◽  
Vol 340 ◽  
pp. 642-646
Author(s):  
Li Song Tian ◽  
Wei Xuan Chen
Keyword(s):  
White Noise ◽  
Spatial Correlation ◽  
Partial Discharge ◽  
Parameters Estimation ◽  
Optimization Strategy ◽  
Signal Parameter ◽  
Detection Systems ◽  
Correlation Algorithm ◽  
White Noises ◽  
General Method

The partial discharge (PD) detection systems are often vulnerable to strong external interferences, and sometimes the PD signals are submerged in noises (white noise for example) completely. So the signals acquired must be preprocessed to obtain the reliable PD information. While there are many methods for white noise denoising, mostly are not very suitable for partial discharge. The wavelet transform (WT) coefficient of PD and white noises have different spread characteristics in different WT scales. Based on the Information Theory, The Minimum Information Description Length (MDL) criterion is a optimization strategy, a small amount of signal parameter is requried to the PD signals representation, the paper proposes a wavelet spatial correlation algorithm to partial discharge denoising based on MDL criterion: optimal wavelet function is selected based on MDL, then have the white noise reduced in WT, the algorithm has wonderful virtues such as free from any parameters estimation about noise, free from presetting threshhold and threshold chooseing behavior, so the algorithm is highly adaptive. Large amount of experimental results illustrate that the method presented in this paper are efficient and feasible and outperforms other general method of PD noise reduction.

scholarly journals A Low-Rank Tensor Model for Hyperspectral Image Sparse Noise Removal

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 62120-62127 ◽  
Author(s):  
Lizhen Deng ◽  
Hu Zhu ◽  
Yujie Li ◽  
Zhen Yang
Keyword(s):  
Hyperspectral Image ◽  
Noise Removal ◽  
Low Rank ◽  
Tensor Model ◽  
Rank Tensor

scholarly journals Wavelet-Based Weighted Median Filter For Image Denoising Of MRI Brain Images

2018 ◽  
Vol 10 (1) ◽  
pp. 201 ◽  
Author(s):  
N. Rajalakshmi ◽  
K. Narayanan ◽  
P. Amudhavalli
Keyword(s):  
Additive White Gaussian Noise ◽  
Median Filter ◽  
Wiener Filter ◽  
Brain Images ◽  
Visual Appearance ◽  
Mri Brain ◽  
Weighted Median ◽  
Weighted Median Filter ◽  
Mri Brain Images

<p>Preliminary diagnosing of MRI images from the hospital cannot be relied on because of the chances of occurrence of artifacts resulting in degraded quality of image, while others may be confused with pathology. Obtained MRI image usually contains limited artifacts. It becomes complex one for doctors in analyzing them. By increasing the contrast of an image, it will be easy to analyze. In order to find the tumor part efficiently MRI brain image should be enhanced properly. The image enhancement methods mainly improve the visual appearance of MRI images. The goal of denoising is to remove the noise, which may corrupt an image during its acquisition or transmission, while retaining its quality. In this paper effectiveness of seven denoising algorithms viz. median filter, wiener filter, wavelet filter, wavelet based wiener, NLM, wavelet based NLM, proposed wavelet based weighted median filter(WMF) using MRI images in the presence of additive white Gaussian noise is compared. The experimental results are analyzed in terms of various image quality metrics.</p>

scholarly journals Development of Accurate BSIM4 Noise Parameters for CMOS 0.13-µm Transistors in Below 3-GHz LNA Application

2018 ◽  
Vol 10 (3) ◽  
pp. 925
Author(s):  
Asmaa Nur Aqilah Zainal Badri ◽  
Norlaili Mohd Noh ◽  
Shukri Bin Korakkottil Kunhi Mohd ◽  
Asrulnizam Abd Manaf ◽  
Arjuna Marzuki ◽  
...  
Keyword(s):  
Thermal Noise ◽  
Noise Figure ◽  
Noise Model ◽  
Ic Design ◽  
Noise Parameters ◽  
Specific Frequency ◽  
Minimum Noise ◽  
Minimum Noise Figure ◽  
Selection Of

Accurate transistor thermal noise model is crucial in IC design as it allows accurate selection of transistors for specific frequency application. The accuracy of the model is represented by the similarity between the simulated and the measured noise parameters (NPs). This work was based on a problem faced by a foundry concerning the dissimilarities between the measured and simulated NPs, especially minimum noise figure (NF<sub>min</sub>) for frequencies below 3 GHz.

Spatial Resolution Enhancement for Hyperspectral Image Using Gaussian Scale Mixtures

2015 ◽  
Vol 713-715 ◽  
pp. 1926-1930 ◽  
Author(s):  
Jie Liu ◽  
Yi Fan Zhang
Keyword(s):  
Spatial Resolution ◽  
Hyperspectral Image ◽  
Resolution Enhancement ◽  
Gaussian Model ◽  
Practical Implementation ◽  
Simulation Experiments ◽  
Gaussian Scale Mixture ◽  
Bayesian Fusion ◽  
Implementation Scheme ◽  
Fusion Framework

In this paper, a wavelet-based Bayesian fusion framework is presented, in which a low spatial resolution hyperspectral (HS) image is fused with a high spatial resolution multispectral (MS) image. Particularly, a multivariate model, Gaussian Scale Mixture (GSM) model, is employed, which is believed to be capable of modeling the distribution of wavelet coefficients more accurately. A practical implementation scheme is also presented for feasible calculations. The proposed approach is validated by simulation experiments for HS and MS image fusion. The experimental results of the proposed approach are also compared with its counterpart employing a Gaussian model for performance evaluation.

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