Application of Adaptive Hypergraph Model to Impulsive Noise Detection
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Impulse noise removal utilizing second-order difference analysis
Signal Processing
Fuzzy vector partition filtering technique for color image restoration
Computer Vision and Image Understanding
Impulsive noise suppression from images with the noise exclusive filter
EURASIP Journal on Applied Signal Processing
Image restoration based on Laplacian preprocessed long-range correlation
Multidimensional Systems and Signal Processing
Root Mean Square filter for noisy images based on hyper graph model
Image and Vision Computing
Training cellular automata for image processing
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Hypergraph-Based image representation
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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An adaptive median based filter is proposed for removing noise from images. Specifically, the observed sample vector at each pixel location is classified into one of M mutually exclusive partitions, each of which has a particular filtering operation. The observation signal space is partitioned based an the differences defined between the current pixel value and the outputs of CWM (center weighted median) filters with variable center weights. The estimate at each location is formed as a linear combination of the outputs of those CWM filters and the current pixel value. To control the dynamic range of filter outputs, a location-invariance constraint is imposed upon each weighting vector. The weights are optimized using the constrained LMS (least mean square) algorithm. Recursive implementation of the new filter is then addressed. The new technique consistently outperforms other median based filters in suppressing both random-valued and fixed-valued impulses, and it also works satisfactorily in reducing Gaussian noise as well as mixed Gaussian and impulse noise