Edge-preserving artifact-free smoothing with image pyramids
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Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
Speckle noise reduction in SAS imagery
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A versatile technique for visual enhancement of medical ultrasound images
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Filtering noise on mammographic phantom images using local contrast modification functions
Image and Vision Computing
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
SAR imagery segmentation by statistical region growing and hierarchical merging
Digital Signal Processing
Image postprocessing by Non-local Kuan's filter
Journal of Visual Communication and Image Representation
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
Object recognition of a mobile robot based on SIFT with de-speckle filtering
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
A comparative evaluation of various de-speckling algorithms for medical images
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Despeckling low SNR, low contrast ultrasound images via anisotropic level set diffusion
Multidimensional Systems and Signal Processing
Computer Vision and Image Understanding
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In this paper, we consider the restoration of images with signal-dependent noise. The filter is noise smoothing and adapts to local changes in image statistics based on a nonstationary mean, nonstationary variance (NMNV) image model. For images degraded by a class of uncorrelated, signal-dependent noise without blur, the adaptive noise smoothing filter becomes a point processor and is similar to Lee's local statistics algorithm [16]. The filter is able to adapt itself to the nonstationary local image statistics in the presence of different types of signal-dependent noise. For multiplicative noise, the adaptive noise smoothing filter is a systematic derivation of Lee's algorithm with some extensions that allow different estimators for the local image variance. The advantage of the derivation is its easy extension to deal with various types of signal-dependent noise. Film-grain and Poisson signal-dependent restoration problems are also considered as examples. All the nonstationary image statistical parameters needed for the filter can be estimated from the noisy image and no a priori information about the original image is required.