An Adaptive Wavelet Shrinkage and its Application in Image De-Noising

2011 ◽  
Vol 128-129 ◽  
pp. 500-503
Author(s):  
Tian Jie Cao
Keyword(s):  
Linear Function ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Computing Time ◽  
Adaptive Method ◽  
Risk Estimate ◽  
Wavelet Coefficients ◽  
Adaptive Wavelet ◽  
Proportional Relation ◽  
Unbiased Risk

In this paper an adaptive method of shrinkage of the wavelet coefficients is presented. In the method, the wavelet coefficients are divided into two classes by a threshold. One class of them with the smaller absolute values at a scale is transformed with a proportional relation，another class with the larger absolute values at the same scale is transformed with a linear function. The threshold and the coefficient in the proportional relation or in the linear function are determined by the principle of minimizing the Stein’s unbiased risk estimate. In the paper, the method of estimation of the threshold and the coefficient is given and the adaptive method of shrinkage of the wavelet coefficients is applied to image denoising. Examples in the paper show that the presented method has an advantage over SureShrink from the point of view of both the Stein’s unbiased risk estimate and the signal-to-noise ratio. In addition, the method takes almost the same computing time as the SureShrink in image denoising.

Minimum unbiased risk estimate based 2DPCA for color image denoising

2021 ◽  
Author(s):  
Mingli Wang ◽  
Xinwei Jiang ◽  
Junbin Gao ◽  
Tianjiang Wang ◽  
Chunlong Hu ◽  
...  
Keyword(s):  
Image Denoising ◽  
Color Image ◽  
Risk Estimate ◽  
Color Image Denoising ◽  
Unbiased Risk

IMAGE DENOISING BASED ON WAVELET SHRINKAGE USING NEIGHBOR AND LEVEL DEPENDENCY

2009 ◽  
Vol 07 (03) ◽  
pp. 299-311 ◽  
Author(s):  
DONGWOOK CHO ◽  
TIEN D. BUI ◽  
GUANGYI CHEN
Keyword(s):  
Signal To Noise Ratio ◽  
Signal Denoising ◽  
Wavelet Thresholding ◽  
Wavelet Coefficients ◽  
Wavelet Shrinkage ◽  
Minimum Risk ◽  
Shrinkage Methods ◽  
Risk Estimator ◽  
Visual Artifacts ◽  
Unbiased Risk

Since Donoho et al. proposed the wavelet thresholding method for signal denoising, many different denoising approaches have been suggested. In this paper, we present three different wavelet shrinkage methods, namely NeighShrink, NeighSure and NeighLevel. NeighShrink thresholds the wavelet coefficients based on Donoho's universal threshold and the sum of the squares of all the wavelet coefficients within a neighborhood window. NeighSure adopts Stein's unbiased risk estimator (SURE) instead of the universal threshold of NeighShrink so as to obtain the optimal threshold with minimum risk for each subband. NeighLevel uses parent coefficients in a coarser level as well as neighbors in the same subband. We also apply a multiplying factor for the optimal universal threshold in order to get better denoising results. We found that the value of the constant is about the same for different kinds and sizes of images. Experimental results show that our methods give comparatively higher peak signal to noise ratio (PSNR), are much more efficient and have less visual artifacts compared to other methods.

scholarly journals DENOISING OF LIDAR ECHO SIGNAL BASED ON WAVELET ADAPTIVE THRESHOLD METHOD

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 215-220
Author(s):  
S. H. Long ◽  
G. Q. Zhou ◽  
H. Y. Wang ◽  
X. Zhou ◽  
J. L. Chen ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Adaptive Threshold ◽  
Threshold Function ◽  
Threshold Method ◽  
Denoising Method ◽  
Adaptive Wavelet ◽  
Signal Characteristics ◽  
Soft Threshold ◽  
Two Parameters ◽  
Unbiased Risk

Abstract. The wavelet threshold method is widely used in signal denoising. However, traditional hard threshold method or soft threshold method is deficient for depending on fixed threshold and instability. In order to achieve efficient denoising of echo signals, an adaptive wavelet threshold denoising method, absorbing the advantages of the hard threshold and the soft threshold, is proposed. Based on the advantages of traditional threshold method, new threshold function is continuous, steerable and flexibly changeable by adjusting two parameters. The threshold function is flexibly changed between the hard threshold and the soft threshold function by two parameter adjustments. According to the Stein unbiased risk estimate (SURE), this new method can determine thresholds adaptively. Adopting different thresholds adaptively at different scales, this method can automatically track noise, which can effectively remove the noise on each scale. Therefore, the problems of noise misjudgement and incomplete denoising can be solved, to some extent, in the process of signal processing. The simulation results of MATLAB show that compared with hard threshold method and soft threshold method, the signal-to-noise ratio (SNR) of the proposed de-noising method is increased by nearly 2dB, and 4dB respectively. It is safely to conclude that, when background noise eliminated, the new wavelet adaptive threshold method preserves signal details effectively and enhances the separability of signal characteristics.

scholarly journals A New Image Denoising Method Based on Adaptive Multiscale Morphological Edge Detection

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Gang Wang ◽  
Zesong Wang ◽  
Jinhai Liu
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Gibbs Phenomenon ◽  
Structural Similarity ◽  
Threshold Function ◽  
Wavelet Coefficients ◽  
Denoising Method ◽  
Image Edge

Wavelet transform is an effective method for removal of noise from image. But traditional wavelet transform cannot improve the smooth effect and reserve image’s precise details simultaneously; even false Gibbs phenomenon can be produced. This paper proposes a new image denoising method based on adaptive multiscale morphological edge detection beyond the above limitation. Firstly, the noisy image is decomposed by using one wavelet base. Then, the image edge is detected by using the adaptive multiscale morphological edge detection based on the wavelet decomposition. On this basis, wavelet coefficients belonging to the edge position are dealt with with the improved wavelet domain wiener filtering, and the others are dealt with with the improved Bayesian threshold and the improved threshold function. Finally, wavelet coefficients are inversely processed to obtain the denoised image. Experimental results show that this method can effectively remove the image noise without blurring edges and highlight the characteristics of image edge at the same time. The validation results of the denoised images with higher peak signal to noise ratio (PSNR) and structural similarity (SSIM) demonstrate their robust capability for real applications in the future.

New self-adaptive method for image denoising based on sparse decomposition and clustering

2013 ◽  
Vol 33 (2) ◽  
pp. 476-479
Author(s):  
Yali WEI ◽  
Xianbin WEN ◽  
Yongliao ZOU ◽  
Yongchun ZHENG
Keyword(s):  
Image Denoising ◽  
Adaptive Method ◽  
Sparse Decomposition ◽  
Self Adaptive

A fast and adaptive method for complex-valued SAR image denoising based on l k norm regularization

2009 ◽  
Vol 52 (1) ◽  
pp. 138-148 ◽  
Author(s):  
WeiWei Wang ◽  
ZhengMing Wang ◽  
ZhenYu Yuan ◽  
MingShan Li
Keyword(s):  
Image Denoising ◽  
Adaptive Method ◽  
Sar Image ◽  
Complex Valued

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.

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