Speckle noise reduction in SAS imagery
Signal Processing
Fuzzy vector partition filtering technique for color image restoration
Computer Vision and Image Understanding
An adaptive window mechanism for image smoothing
Computer Vision and Image Understanding
Technical Section: Circular spatial filtering under high-noise-variance conditions
Computers and Graphics
Automatic noise estimation in images using local statistics. Additive and multiplicative cases
Image and Vision Computing
Image denoising in steerable pyramid domain based on a local Laplace prior
Pattern Recognition
Speckle reduction by adaptive window anisotropic diffusion
Signal Processing
Noise reduction in computerized tomography images by means of polynomial transforms
Journal of Visual Communication and Image Representation
Restoration of images degraded by compound noise sources using Markov random field models
Journal of Visual Communication and Image Representation
Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method
Pattern Recognition
SAR imagery segmentation by statistical region growing and hierarchical merging
Digital Signal Processing
Expert Systems with Applications: An International Journal
Stochastic image denoising based on Markov-chain Monte Carlo sampling
Signal Processing
Adaptive sampling and reconstruction using greedy error minimization
Proceedings of the 2011 SIGGRAPH Asia Conference
Fast algorithm for multiplicative noise removal
Journal of Visual Communication and Image Representation
Image denoising using complex wavelets and markov prior models
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Soft-Switching adaptive technique of impulsive noise removal in color images
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
True 4D image denoising on the GPU
Journal of Biomedical Imaging - Special issue on Parallel Computation in Medical Imaging Applications
A non-Newtonian gradient for contour detection in images with multiplicative noise
Pattern Recognition Letters
Pulse coupled neural network based anisotropic diffusion method for 1/f noise reduction
Mathematical and Computer Modelling: An International Journal
A comparative evaluation of various de-speckling algorithms for medical images
Proceedings of the CUBE International Information Technology Conference
A Robust Method for Ventriculomegaly Detection from Neonatal Brain Ultrasound Images
Journal of Medical Systems
Automatic closed edge detection using level lines selection
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Directionlet-based denoising of SAR images using a Cauchy model
Signal Processing
Gradient-based Wiener filter for image denoising
Computers and Electrical Engineering
Medical image denoising using adaptive fusion of curvelet transform and total variation
Computers and Electrical Engineering
Computer Methods and Programs in Biomedicine
GPU-accelerated 3D mipmap for real-time visualization of ultrasound volume data
Computers in Biology and Medicine
Journal of Visual Communication and Image Representation
Multiple-step local Wiener filter with proper stopping in wavelet domain
Journal of Visual Communication and Image Representation
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
Natural Computing: an international journal
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Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays are developed based on their local mean and variance. These algorithms are nonrecursive and do not require the use of any kind of transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in real-time digital image processing applications and where a parallel processor can be used. For both the additive and multiplicative cases, the a priori mean and variance of each pixel is derived from its local mean and variance. Then, the minimum mean-square error estimator in its simplest form is applied to obtain the noise filtering algorithms. For multiplicative noise a statistical optimal linear approximation is made. Experimental results show that such an assumption yields a very effective filtering algorithm. Examples on images containing 256 脳 256 pixels are given. Results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.