In this paper, a new approach to the problem of impulsive noise reduction in image is presented. First, an image neighborhood hypergraph representation using a similarity measure is computed. Next, a detection procedure based on hypergraph properties is used to classify hyperedges either as noisy, or clean data. Then we apply a nonlinear filter to noisy detected pixels. The results show that the proposed method outperforms most of the basic algorithms for the reduction of impulsive noise.
Similarity hypergraph representation for impulsive noise reduction
01.01.2003
307500 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Similarity Hypergraph Representation for Impulsive Noise Reduction
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