scholarly journals Sentinel-1 additive noise removal from cross-polarization extra-wide TOPSAR with dynamic least-squares

2020 ◽  
Vol 248 ◽  
pp. 111982
Author(s):  
Peter Q. Lee ◽  
Linlin Xu ◽  
David A. Clausi
Keyword(s):  
Least Squares ◽  
Additive Noise ◽  
Noise Removal ◽  
Cross Polarization

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

scholarly journals Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 938
Author(s):  
Hyunho Choi ◽  
Jechang Jeong
Keyword(s):  
Additive Noise ◽  
Low Frequency ◽  
Speckle Noise ◽  
Image Filtering ◽  
Noise Removal ◽  
Discrete Wavelet ◽  
Ultrasound Images ◽  
Noise Elimination ◽  
Residual Noise ◽  
Guided Image Filtering

Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods.

Efficient Thermal Noise Removal for Sentinel-1 TOPSAR Cross-Polarization Channel

2018 ◽  
Vol 56 (3) ◽  
pp. 1555-1565 ◽  
Author(s):  
Jeong-Won Park ◽  
Anton A. Korosov ◽  
Mohamed Babiker ◽  
Stein Sandven ◽  
Joong-Sun Won
Keyword(s):  
Thermal Noise ◽  
Noise Removal ◽  
Cross Polarization ◽  
Polarization Channel

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 Shock filter coupled with a high-order PDE for additive noise removal and image quality enhancement

Array ◽  
2021 ◽  
pp. 100105
Author(s):  
Simo Thierry ◽  
Welba Colince ◽  
Ntsama Eloundou Pascal ◽  
Noura Alexendre
Keyword(s):  
Image Quality ◽  
Additive Noise ◽  
Noise Removal ◽  
High Order ◽  
Quality Enhancement ◽  
Image Quality Enhancement ◽  
Shock Filter

Improved Tooth Crown Edge Smoothing Method Based on Noise Classification and Fitting

2020 ◽  
Vol 10 (11) ◽  
pp. 2609-2619
Author(s):  
Tian Ma ◽  
Yurong Li
Keyword(s):  
3D Reconstruction ◽  
Least Squares ◽  
Rate Increase ◽  
Removal Rate ◽  
Noise Removal ◽  
New Method ◽  
Boundary Line ◽  
Tooth Crown ◽  
Least Squares Fitting ◽  
Noise Classification

Because three-dimensional (3D) models of teeth are currently obtained via oral scanning, there are only the tooth crown and gingival surface part, lack of data on the roots of teeth, which is not conducive to the 3D reconstruction of teeth. In order to help doctors to carry out virtual tooth correction, this paper studies the edge characteristics of the tooth crown model, removes the edge noise, which can better carry out the 3D reconstruction of teeth. Therefore, this paper proposes an improved method of tooth crown edge smoothing based on noise classification and fitting. First, according to the characteristics of the tooth crown edge, the method of noise classification is proposed after fitting analysis. The noise can be divided into two types: the noise in the boundary line and the noise in the fitting curve. Then, the noise can be identified according to the Gaussian curvature. Finally, the improved Laplacian smoothing and least squares fitting methods are used to remove the two types of noise, and the denoised tooth crown model is the output. The smoothing effect of the method is verified in terms of the noise removal rate, the patch filling rate, and the patch deletion rate. Compared with the traditional Laplacian smoothig, the new method exhibited a noise removal rate increase of 86.0%, a probability of patch filling that approximately doubled, and a probability of patch deletion that basically remained the same. Compared with the least squares fitting method, the new method exhibited a noise removal rate increase of 75.9%, a patch filling reduction of 22.61%, and a patch deletion reduction of 22.14%.

Sign in / Sign up

Export Citation Format

Share Document