Paper Info
Title
On Nearest Neighbors in Non Local Means Denoising.
Abstract
To denoise a reference patch, the Non-Local-Means denoising filter processes a set of neighbor patches. Few Nearest Neighbors (NN) are used to limit the computational burden of the algorithm. Here here we show analytically that the NN approach introduces a bias in the denoised patch, and we propose a different neighborsu0027 collection criterion, named Statistical NN (SNN), to alleviate this issue. Our approach outperforms the traditional one in case of both white and colored noise: fewer SNNs generate images of higher quality, at a lower computational cost.
Year
Venue
Field
2017
arXiv: Computer Vision and Pattern Recognition
Noise reduction,Colors of noise,Pattern recognition,Non-local means,Computer science,Artificial intelligence,Machine learning
DocType
Volume
Citations 
Journal
abs/1711.07568
0
PageRank 
References 
Authors
0.34
6
2
Name
Order
Citations
PageRank
Iuri Frosio120615.25
Jan Kautz23615198.77