Image denoising with window shrink wavelet coefficients by adaptive threshold

2007 ◽  
Author(s):  
Yifan Zhao ◽  
Jiuxian Li ◽  
Liangzheng Xia
Keyword(s):  
Image Denoising ◽  
Adaptive Threshold ◽  
Wavelet Coefficients

scholarly journals 2D–1D Wavelet Reconstruction as a Tool for Source Finding in Spectroscopic Imaging Surveys

2012 ◽  
Vol 29 (3) ◽  
pp. 244-250 ◽  
Author(s):  
L. Flöer ◽  
B. Winkel
Keyword(s):  
Image Enhancement ◽  
Image Denoising ◽  
Spectroscopic Data ◽  
Wavelet Decomposition ◽  
Spectroscopic Imaging ◽  
Basic Method ◽  
Spectral Dimension ◽  
Wavelet Coefficients ◽  
Data Cubes ◽  
Spatial Dimensions

AbstractToday, image denoising by thresholding of wavelet coefficients is a commonly used tool for 2D image enhancement. Since the data product of spectroscopic imaging surveys has two spatial dimensions and one spectral dimension, the techniques for denoising have to be adapted to this change in dimensionality. In this paper we will review the basic method of denoising data by thresholding wavelet coefficients and implement a 2D–1D wavelet decomposition to obtain an efficient way of denoising spectroscopic data cubes. We conduct different simulations to evaluate the usefulness of the algorithm as part of a source finding pipeline.

scholarly journals An adaptive wavelet transformation filtering algorithm for improving road anomaly detection and characterization in vehicular technology

2019 ◽  
Vol 9 (5) ◽  
pp. 3664
Author(s):  
Habeeb Bello-Salau ◽  
A. J. Onumanyi ◽  
B. O. Sadiq ◽  
H. Ohize ◽  
A. T. Salawudeen ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
False Alarm ◽  
Wavelet Transformation ◽  
Multiple Scales ◽  
Detection System ◽  
Spatial Filter ◽  
Adaptive Threshold ◽  
Wavelet Coefficients ◽  
Adaptive Wavelet ◽  
Vehicular Technology

Accelerometers are widely used in modern vehicular technologies to automatically detect and characterize road anomalies such as potholes and bumps. However, measurements from an accelerometer are usually plagued by high noise levels, which typically increase the false alarm and misdetection rates of an anomaly detection system. To address this problem, we have developed in this paper an adaptive threshold estimation technique to filter accelerometer measurements effectively to improve road anomaly detection and characterization in vehicular technologies. Our algorithm decomposes the output signal of an accelerometer into multiple scales using wavelet transformation (WT). Then, it correlates the wavelet coefficients across adjacent scales and classifies them using a newly proposed adaptive threshold technique. Furthermore, our algorithm uses a spatial filter to smoothen further the correlated coefficients before using these coefficients to detect road anomalies. Our algorithm then characterizes the detected road anomalies using two unique features obtained from the filtered wavelet coefficients to differentiate potholes from bumps. The findings from several comparative tests suggest that our algorithm successfully detects and characterizes road anomalies with high levels of accuracy, precision and low false alarm rates as compared to other known methods.

Characterization of local regions for wavelet-based image denoising using a statistical approach

2020 ◽  
Vol 18 (03) ◽  
pp. 2050011
Author(s):  
Rajiv Verma ◽  
Rajoo Pandey
Keyword(s):  
Image Denoising ◽  
Statistical Approach ◽  
Wavelet Coefficient ◽  
Threshold Value ◽  
Vital Role ◽  
Wiener Filtering ◽  
Original Signal ◽  
Wavelet Coefficients ◽  
Local Window

The shape of local window plays a vital role in the estimation of original signal variance, which is used to shrink the noisy wavelet coefficients in wavelet-based image denoising algorithms. This paper presents an anisotropic-shaped region-based Wiener filtering (ASRWF) and BayesShrink (ASRBS) algorithms, which exploit the region characteristics to estimate the original signal variance using a statistical approach. The proposed approach divides the region centered on a noisy wavelet coefficient into various non-overlapping subregions. The Euclidean distance-based measure is considered to obtain the similarities between reference subregion and adjacent subregions. An appropriate threshold value is estimated by applying a statistical approach on these distances and the sets of similar and dissimilar subregions are obtained from a defined region. Thus, an anisotropic-shaped region is obtained by neglecting the dissimilar subregions in a defined region. The variance of every similar subregion is calculated and then averaged to estimate the original signal variance to shrink noisy wavelet coefficients effectively. Finally, the estimated signal variance is utilized in Wiener filtering and BayesShrink algorithms to improve the denoising performance. The performance of the proposed algorithms is analyzed qualitatively and quantitatively on standard images for different noise levels.

Image Denoising with Contour-Based Directionlet Transform

2014 ◽  
Vol 513-517 ◽  
pp. 3607-3611
Author(s):  
Huan An Xu ◽  
Guo Hua Peng ◽  
Zhe Liu
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Numerical Experiments ◽  
Adaptive Threshold ◽  
Perceptual Quality ◽  
Laplacian Pyramid ◽  
Low Frequencies ◽  
Threshold Algorithm ◽  
Image Transform

A novel mutiscale and directionally adaptive image transform called contour based directionlet tansform is presented. Directionlet transform (DT) has shown its charming performance in image processing, but it has scrambled frequencies. Laplacian Pyramid is employed here to separate the low frequencies before applying DT for avoiding the drawback. And an adaptive threshold algorithm is proposed for denoising. Numerical experiments are performed to assess the applicability of the proposed method. The obtained results show that the proposed scheme outperforms Wavelet and Directionlet transforms in terms of numerical and perceptual quality.

Vector processing of wavelet coefficients for robust image denoising

2001 ◽  
Vol 19 (7) ◽  
pp. 435-450 ◽  
Author(s):  
M.E. Zervakis ◽  
V. Sundararajan ◽  
K.K. Parhi
Keyword(s):  
Image Denoising ◽  
Wavelet Coefficients ◽  
Vector Processing ◽  
Robust Image
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