Graphs and algorithms
Finite topology as applied to image analysis
Computer Vision, Graphics, and Image Processing
Combinatorics and image processing
Graphical Models and Image Processing
The image processing handbook (3rd ed.)
The image processing handbook (3rd ed.)
Noise Detection and Cleaning by Hypergraph Model
ITCC '00 Proceedings of the The International Conference on Information Technology: Coding and Computing (ITCC'00)
Application of partition-based median type filters for suppressing noise in images
IEEE Transactions on Image Processing
Weighted adaptive neighborhood hypergraph partitioning for image segmentation
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Hypergraph-Based image representation
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Random walks in directed hypergraphs and application to semi-supervised image segmentation
Computer Vision and Image Understanding
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In this paper, using hypergraph theory, we introduce an image model called Adaptive Image Neighborhood Hypergraph (AINH). From this model we propose a combinatorial definition of noisy data. A detection procedure is used to classify the hyperedges either as noisy or clean data. Similar to other techniques, the proposed algorithm uses an estimation procedure to remove the effects of the noise. Extensive simulations show that the proposed scheme consistently works well in suppressing of impulsive noise.