TY - GEN
T1 - Shock filters based on implicit cluster separation
AU - Namboodiri, Vinay P.
AU - Chaudhuri, Subhasis
PY - 2005/7/25
Y1 - 2005/7/25
N2 - One of the classic problems in low level vision is image restoration. An important contribution toward this effort has been the development of shock filters by Osher and Rudin [15]. It performs image de-blurring using hyperbolic partial differential equations. In this paper we relate the notion of cluster separation from the field of pattern recognition to the shock filter formulation. A kind of shock filter is proposed based on the idea of gradient based separation of clusters. The proposed formulation is general enough as it can allow various models of density functions in the cluster separation process. The efficacy of the method is demonstrated through various examples.
AB - One of the classic problems in low level vision is image restoration. An important contribution toward this effort has been the development of shock filters by Osher and Rudin [15]. It performs image de-blurring using hyperbolic partial differential equations. In this paper we relate the notion of cluster separation from the field of pattern recognition to the shock filter formulation. A kind of shock filter is proposed based on the idea of gradient based separation of clusters. The proposed formulation is general enough as it can allow various models of density functions in the cluster separation process. The efficacy of the method is demonstrated through various examples.
UR - http://www.scopus.com/inward/record.url?scp=24644457198&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2005.322
DO - 10.1109/CVPR.2005.322
M3 - Chapter in a published conference proceeding
AN - SCOPUS:24644457198
SN - 0769523722
SN - 9780769523729
T3 - Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
SP - 82
EP - 87
BT - Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
PB - IEEE
T2 - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
Y2 - 20 June 2005 through 25 June 2005
ER -