TY - GEN
T1 - Local jet based similarity for NL-means filtering
AU - Manzanera, Antoine
PY - 2010/11/18
Y1 - 2010/11/18
N2 - Reducing the dimension of local descriptors in images is useful to perform pixels comparison faster. We show here that, for computing the NL-means denoising filter, image patches can be favourably replaced by a vector of spatial derivatives (local jet), to calculate the similarity between pixels. First, we present the basic, limited range implementation, and compare it with the original NL-means. We use a fast estimation of the noise variance to automatically adjust the decay parameter of the filter. Next, we present the unlimited range implementation using nearest neighbours search in the local jet space, based on a binary search tree representation.
AB - Reducing the dimension of local descriptors in images is useful to perform pixels comparison faster. We show here that, for computing the NL-means denoising filter, image patches can be favourably replaced by a vector of spatial derivatives (local jet), to calculate the similarity between pixels. First, we present the basic, limited range implementation, and compare it with the original NL-means. We use a fast estimation of the noise variance to automatically adjust the decay parameter of the filter. Next, we present the unlimited range implementation using nearest neighbours search in the local jet space, based on a binary search tree representation.
KW - Image denoising
KW - Local jet
KW - NL-mean
KW - Nearest neighbour search
U2 - 10.1109/ICPR.2010.654
DO - 10.1109/ICPR.2010.654
M3 - Conference contribution
AN - SCOPUS:78149472631
SN - 9780769541099
T3 - Proceedings - International Conference on Pattern Recognition
SP - 2668
EP - 2671
BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
T2 - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Y2 - 23 August 2010 through 26 August 2010
ER -