Speckle reduction by adaptive window anisotropic diffusion
Signal Processing
Multiplicative Noise Cleaning via a Variational Method Involving Curvelet Coefficients
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Coefficient-Tracking Speckle Reducing Anisotropic Diffusion
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Nonlocal means-based speckle filtering for ultrasound images
IEEE Transactions on Image Processing
Noise-driven anisotropic diffusion filtering of MRI
IEEE Transactions on Image Processing
Efficient implementation for spherical flux computation and its application to vascular segmentation
IEEE Transactions on Image Processing
Removing Multiplicative Noise by Douglas-Rachford Splitting Methods
Journal of Mathematical Imaging and Vision
Multiplicative Noise Removal Using L1 Fidelity on Frame Coefficients
Journal of Mathematical Imaging and Vision
Ramp preserving Perona-Malik model
Signal Processing
Anisotropic diffusion for preserving boundary-edge
ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
Homogeneity similarity based image denoising
Pattern Recognition
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
A weberized total variation regularization-based image multiplicative noise removal algorithm
EURASIP Journal on Advances in Signal Processing
Multiplicative noise removal via a novel variational model
Journal on Image and Video Processing - Special issue on emerging methods for color image and video quality enhancement
Fast algorithm for multiplicative noise removal
Journal of Visual Communication and Image Representation
Ultrasound speckle reduction via super resolution and nonlinear diffusion
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Objective comparison of contour detection in noisy images
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A non-Newtonian gradient for contour detection in images with multiplicative noise
Pattern Recognition Letters
Monte Carlo despeckling of transrectal ultrasound images of the prostate
Digital Signal Processing
A new similarity measure for nonlocal filtering in the presence of multiplicative noise
Computational Statistics & Data Analysis
On the choice of the parameters for anisotropic diffusion in image processing
Pattern Recognition
On the impact of anisotropic diffusion on edge detection
Pattern Recognition
Computers & Mathematics with Applications
An effective dual method for multiplicative noise removal
Journal of Visual Communication and Image Representation
Despeckling low SNR, low contrast ultrasound images via anisotropic level set diffusion
Multidimensional Systems and Signal Processing
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Ultrasound imaging systems provide the clinician with noninvasive, low-cost, and real-time images that can help them in diagnosis, planning, and therapy. However, although the human eye is able to derive the meaningful information from these images, automatic processing is very difficult due to noise and artifacts present in the image. The speckle reducing anisotropic diffusion filter was recently proposed to adapt the anisotropic diffusion filter to the characteristics of the speckle noise present in the ultrasound images and to facilitate automatic processing of images. We analyze the properties of the numerical scheme associated with this filter, using a semi-explicit scheme. We then extend the filter to a matrix anisotropic diffusion, allowing different levels of filtering across the image contours and in the principal curvature directions. We also show a relation between the local directional variance of the image intensity and the local geometry of the image, which can justify the choice of the gradient and the principal curvature directions as a basis for the diffusion matrix. Finally, different filtering techniques are compared on a 2-D synthetic image with two different levels of multiplicative noise and on a 3-D synthetic image of a Y-junction, and the new filter is applied on a 3-D real ultrasound image of the liver