Automatic noise estimation in images using local statistics. Additive and multiplicative cases
Image and Vision Computing
Speckle reduction by adaptive window anisotropic diffusion
Signal Processing
Coefficient-Tracking Speckle Reducing Anisotropic Diffusion
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Noise-driven anisotropic diffusion filtering of MRI
IEEE Transactions on Image Processing
Removing Multiplicative Noise by Douglas-Rachford Splitting Methods
Journal of Mathematical Imaging and Vision
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
A level set filter for speckle reduction in SAR images
EURASIP Journal on Advances in Signal Processing - Special issue on advances in multidimensional synthetic aperture radar 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
Generating fuzzy edge images from gradient magnitudes
Computer Vision and Image Understanding
Fuzzy diffusion filter with extended neighborhood
Expert Systems with Applications: An International Journal
Characteristic matching-based adaptive fast bilateral filter for ultrasound speckle reduction
Pattern Recognition Letters
On the impact of anisotropic diffusion on edge detection
Pattern Recognition
Despeckling low SNR, low contrast ultrasound images via anisotropic level set diffusion
Multidimensional Systems and Signal Processing
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In this paper, we focus on the problem of speckle removal by means of anisotropic diffusion and, specifically, on the importance of the correct estimation of the statistics involved. First, we derive an anisotropic diffusion filter that does not depend on a linear approximation of the speckle model assumed, which is the case of a previously reported filter, namely, SRAD. Then, we focus on the problem of estimation of the coefficient of variation of both signal and noise and of noise itself. Our experiments indicate that neighborhoods used for parameter estimation do not need to coincide with those used in the diffusion equations. Then, we show that, as long as the estimates are good enough, the filter proposed here and the SRAD perform fairly closely, a fact that emphasizes the importance of the correct estimation of the coefficients of variation