Image Denoising for Adaptive Threshold Function Based on the Dyadic Wavelet Transform

2009 ◽  
Author(s):  
Zhenghong Huang ◽  
Li Xia
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Adaptive Threshold ◽  
Threshold Function ◽  
Dyadic Wavelet ◽  
Dyadic Wavelet Transform

IMAGE DENOISING BASED ON THE DYADIC WAVELET TRANSFORM AND IMPROVED THRESHOLD

2009 ◽  
Vol 07 (03) ◽  
pp. 269-280 ◽  
Author(s):  
ZHENGHONG HUANG ◽  
BIN FANG ◽  
XIPING HE ◽  
LI XIA
Keyword(s):  
Wavelet Transform ◽  
Theoretical Analysis ◽  
Image Denoising ◽  
The Self ◽  
Threshold Function ◽  
Wavelet Coefficients ◽  
Threshold Method ◽  
Dyadic Wavelet ◽  
Dyadic Wavelet Transform ◽  
Noise Experiment

Wavelet transform has shown a strong capability to filter out noise that existed in images. The main problem to improve denoising performance lies in the fact that it is difficult to select suitable wavelet transform and related thresholds. In this paper, we completely explore characteristics of dyadic wavelet transform and its possible use to image denoising. An improved threshold method is elaborately designed to establish the self-adjusted layer threshold function and wavelet reconstruction. Theoretical analysis shows that denoising precision can be improved by way of adopting different thresholds according to the different scales of the wavelet coefficients of image and noise. Experiment results demonstrated that our method is obviously superior to that of fixed threshold regarding the effect of image denoising.

Estimation of Heart Rate Variability Fluctuations by Wavelet Transform

2011 ◽  
Vol 57 (3) ◽  
pp. 395-400 ◽  
Author(s):  
Anton Popov ◽  
Yevgeniy Karplyuk ◽  
Volodymyr Fesechko
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Wavelet Transform ◽  
Experimental Results ◽  
Dyadic Wavelet ◽  
Heart Rate Fluctuations ◽  
Slow Heart Rate ◽  
Dyadic Wavelet Transform

Estimation of Heart Rate Variability Fluctuations by Wavelet TransformTechnique for separate estimation of fast and slow fluctuations in the heart rate signal is developed. The orthogonal dyadic wavelet transform is used to separate the slow heart rate changes in approximation part of decomposition and fast changes in detail parts. Experimental results using the recordings from persons practicing Chi meditation demonstrated the applicability of estimation heart rate fluctuations with the proposed approach.

scholarly journals Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhuxiang Shen ◽  
Wei Li ◽  
Hui Han
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Similarity Index ◽  
Speckle Noise ◽  
Disease Diagnosis ◽  
Function Optimization ◽  
Threshold Function ◽  
Ultrasound Images ◽  
Better Than

To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.

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