Improvised Curvelet Transform Based Diffusion Filtering for Speckle Noise Removal in Real-Time Vision-Based Database

2020 ◽  
pp. 143-152
Author(s):  
Rohini Mahajan ◽  
Devanand Padha
Keyword(s):  
Real Time ◽  
Curvelet Transform ◽  
Speckle Noise ◽  
Noise Removal ◽  
Diffusion Filtering

Image Denoising Based on Reaction-Diffusion Equation and Curvelet Transform

2010 ◽  
Vol 40-41 ◽  
pp. 554-559
Author(s):  
Yi Mei Song ◽  
Xiao Qing Shang
Keyword(s):  
Image Denoising ◽  
Curvelet Transform ◽  
Reaction Diffusion ◽  
Speckle Noise ◽  
Noise Removal ◽  
Energy Functional ◽  
Reaction Diffusion Equation ◽  
Ill Posed ◽  
Diffusion Function ◽  
Improved Model

To reduce the pseudo-Gibbs effects and the “curvelet like” aliased curves resulted from using curvelet transform for image denoising, we proposed a noise removal method which combines computational harmonic analysis and variation. Firstly, we presented a nonlinear reaction-diffusion digital filter based on Nordström energy functional. For effectively overcoming speckle noise due to the reaction-diffusion process of digital filtering and the ill-posed of diffusion coefficient, we gave an improved model by introducing curvelet smoothing operator and the new diffusion function. Numerical results show that the model is not only for images with Gaussian noise, Salt&pepper noise or Speckle noise, but also suitable for mixed noise, the denoised image has higher PSNR and good visual effect.

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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