Integrating ensemble empirical mode decomposition and nonlinear anisotropic diffusion filter for speckle noise reduction in underwater sonar images

2011 ◽  
Author(s):  
Somayeh Bakhtiari ◽  
Sos Agaian ◽  
Mohammad Jamshidi
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Anisotropic Diffusion ◽  
Speckle Noise ◽  
Ensemble Empirical Mode Decomposition ◽  
Diffusion Filter ◽  
Anisotropic Diffusion Filter ◽  
Mode Decomposition ◽  
Sonar Images ◽  
Speckle Noise Reduction

Adaptive anisotropic diffusion filter for speckle noise reduction for ultrasound images

2013 ◽  
Vol 1 (1) ◽  
pp. 50 ◽  
Author(s):  
Mohammad Motiur Rahman ◽  
Mithun Kumar PK. ◽  
Abdul Aziz ◽  
Md. Gauhar Arefin ◽  
Mohammad Shorif Uddin
Keyword(s):  
Noise Reduction ◽  
Anisotropic Diffusion ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Diffusion Filter ◽  
Anisotropic Diffusion Filter ◽  
Speckle Noise Reduction

Weighted Image Averaging Based Anisotropic Diffusion Denoising Method for Ultrasound Thyroid Image

2020 ◽  
Vol 10 (2) ◽  
pp. 380-390
Author(s):  
Haiyue Zhang ◽  
Daoyun Xu ◽  
Yongbin Qin
Keyword(s):  
Thyroid Disease ◽  
Anisotropic Diffusion ◽  
Averaging Method ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Denoising Method ◽  
Weighted Image ◽  
Diffusion Filter ◽  
Anisotropic Diffusion Filter ◽  
Image Averaging

Thyroid disease is a frequent occurrence in clinical practice and the computerized analysis of ultrasonography has been becoming the most prospective tool for thyroid disease automatic diagnosis. However, the accuracy of vision-based diagnostic analysis is often reduced because the quality of ultrasound image is easily corrupted by the speckle noise. Thus, noise suppression is imperative and significant for the thyroid ultrasonography image preprocessing to increase the reliability of subsequent analysis. In this paper, we propose a novel weighted image averaging method based on anisotropic diffusion filters combination to remove speckle noise and enhance the details of the image at the same time. The method first denoises the image separately by two filters with different performances. The speckle reducing anisotropic diffusion filter can enhance the details of the image, and the anisotropic diffusion filter can better suppress the speckle noise in the image. In order to integrate the advantages of the two filters and reduce the mutual interference meanwhile, an adaptive weighted image averaging method is further proposed to combine the pixels of the two denoised images. The experimental results indicate that the proposed method can achieve promising performance on the template images with various noise levels by considering PSNR and SSIM. What's more, it is not only superior to other methods in automatic segmentation, but also can obtain better visual effect for thyroid images.

Random Noise Reduction Based on Ensemble Empirical Mode Decomposition and Wavelet Threshold Filtering

2012 ◽  
Vol 518-523 ◽  
pp. 3887-3890 ◽  
Author(s):  
Wei Chen ◽  
Shang Xu Wang ◽  
Xiao Yu Chuai ◽  
Zhen Zhang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Reduction Method ◽  
Random Noise ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Eemd Method

This paper presents a random noise reduction method based on ensemble empirical mode decomposition (EEMD) and wavelet threshold filtering. Firstly, we have conducted spectrum analysis and analyzed the frequency band range of effective signals and noise. Secondly, we make use of EEMD method on seismic signals to obtain intrinsic mode functions (IMFs) of each trace. Then, wavelet threshold noise reduction method is used on the high frequency IMFs of each trace to obtain new high frequency IMFs. Finally, reconstruct the desired signal by adding the new high frequency IMFs on the low frequency IMFs and the trend item together. When applying our method on synthetic seismic record and field data we can get good results.

Sign in / Sign up

Export Citation Format

Share Document