A Fourth Order Diffusion Filter for Speckle Noise Removal

2017 ◽  
Author(s):  
Mahipal Jetta ◽  
S. K. Iliyas ◽  
T. Pranihith
Keyword(s):  
Fourth Order ◽  
Speckle Noise ◽  
Noise Removal ◽  
Diffusion Filter

scholarly journals A Multiplicative Noise Removal Approach Based on Partial Differential Equation Model

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Bo Chen ◽  
Jin-Lin Cai ◽  
Wen-Sheng Chen ◽  
Yan Li
Keyword(s):  
Fourth Order ◽  
Multiplicative Noise ◽  
Additive Noise ◽  
Speckle Noise ◽  
Noise Removal ◽  
Equation Model ◽  
Superior Performance ◽  
Pde Model ◽  
Novel Approach ◽  
Fourth Order Pde

Multiplicative noise, also known as speckle noise, is signal dependent and difficult to remove. Based on a fourth-order PDE model, this paper proposes a novel approach to remove the multiplicative noise on images. In practice, Fourier transform and logarithm strategy are utilized on the noisy image to convert the convolutional noise into additive noise, so that the noise can be removed by using the traditional additive noise removal algorithm in frequency domain. For noise removal, a new fourth-order PDE model is developed, which avoids the blocky effects produced by second-order PDE model and attains better edge-preserve ability. The performance of the proposed method has been evaluated on the images with both additive and multiplicative noise. Compared with some traditional methods, experimental results show that the proposed method obtains superior performance on different PSNR values and visual quality.

scholarly journals MFNR: Multi-Frame Method for Complete Speckle Noise Removal in Ultrasound images Using KDE

2020 ◽  
Author(s):  
Awais Nazir ◽  
Muhammad Shahzad Younis ◽  
Muhammad Khurram Shahzad
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
Speckle Noise ◽  
Noise Removal ◽  
General Purpose ◽  
Imaging Systems ◽  
Ultrasound Images ◽  
Frame Method ◽  
Probability Density Function Pdf ◽  
Removal System

Speckle noise is one of the most difficult noises to remove especially in medical applications. It is a nuisance in ultrasound imaging systems which is used in about half of all medical screening systems. Thus, noise removal is an important step in these systems, thereby creating reliable, automated, and potentially low cost systems. Herein, a generalized approach MFNR (Multi-Frame Noise Removal) is used, which is a complete Noise Removal system using KDE (Kernal Density Estimation). Any given type of noise can be removed if its probability density function (PDF) is known. Herein, we extracted the PDF parameters using KDE. Noise removal and detail preservation are not contrary to each other as the case in single-frame noise removal methods. Our results showed practically complete noise removal using MFNR algorithm compared to standard noise removal tools. The Peak Signal to Noise Ratio (PSNR) performance was used as a comparison metric. This paper is an extension to our previous paper where MFNR Algorithm was showed as a general purpose complete noise removal tool for all types of noises

Speckle noise removal applied to ultrasound image of carotid artery based on total least squares model

2016 ◽  
Vol 24 (5) ◽  
pp. 749-760
Author(s):  
Lei Yang ◽  
Jun Lu ◽  
Ming Dai ◽  
Li-Jie Ren ◽  
Wei-Zong Liu ◽  
...  
Keyword(s):  
Carotid Artery ◽  
Least Squares ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Noise Removal ◽  
Total Least Squares
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