A fast impulsive noise color image filter using fuzzy metrics
Real-Time Imaging - Special issue on multi-dimensional image processing
Fast detection and removal of impulsive noise using peer groups and fuzzy metrics
Journal of Visual Communication and Image Representation
Isolating impulsive noise pixels in color images by peer group techniques
Computer Vision and Image Understanding
A Segmentation Method for Digital Images Based on Cluster Analysis
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Fuzzy peer groups for reducing mixed Gaussian-impulse noise from color images
IEEE Transactions on Image Processing
Dynamic Image Segmentation Method Using Hierarchical Clustering
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A new image segmentation algorithm with applications to image inpainting
Computational Statistics & Data Analysis
Two-step fuzzy logic-based method for impulse noise detection in colour images
Pattern Recognition Letters
Rank-Ordered differences statistic based switching vector filter
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
New method for fast detection and removal of impulsive noise using fuzzy metrics
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Edge detection in contaminated images, using cluster analysis
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
A new vector median filter based on fuzzy metrics
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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We introduce a method to restore digital images with contaminated pixels. One particular characteristic of this method is that it does not change the pixels that are not considered contaminated, thus avoiding excessive intervening of the original image. Each pixel is analyzed by studying its eight point neighborhood. A cluster analysis is performed on the group of eight pixels contained in the neighborhood. After deciding how many clusters there are in the neighborhood, a decision is made whether the center pixel is an outlier or not. If so, to assign a new value, another decision is made, on a probabilistic basis, as to which cluster it belongs.This method can be applied to black and white images as well as to color and multiphase images.