Title
Adaptive surface smoothing for enhancement of range data with multiple regularization parameters
Abstract
This paper proposes an adaptive regularized noise smoothing algorithm for range images using the area decreasing flow method, which can preserve meaningful edges during the smoothing process. Adaptation is incorporated by adjusting the regularization parameter according to the results of surface curvature analysis. In general, range data includes mixed noise such as Gaussian or Impulsive noise. Although non-adaptive version of regularized noise smoothing algorithm can easily reduce Gaussian noise, impulsive noise caused by random fluctuation of the sensor acquisition is not easy to be removed from observed range data. It is also difficult to remove noise near edge using the existing adaptive regularization algorithms. In order to cope with the problem, the second smoothness constraint is additionally incorporated into the existing regularization algorithm, which minimizes the difference between the median filtered data and the estimated data. As a result, the proposed algorithm can effectively remove the noise of dense range data while meaningful edge is well-preserved.
Year
DOI
Venue
2004
10.1117/12.526435
Proceedings of SPIE
Keywords
Field
DocType
noise smoothing,regularization,range data,adaptive regularization,Gaussian curvature
Value noise,Mathematical optimization,Median filter,Salt-and-pepper noise,Algorithm,Smoothing,Regularization (mathematics),Adaptive algorithm,Gaussian noise,Mathematics,Gradient noise
Conference
Volume
ISSN
Citations 
5302
0277-786X
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Hyunjong Ki111.72
Jeongho Shin212417.26
Joonki Paik361171.87