DocumentCode :
457499
Title :
Motion Dependent Spatiotemporal Smoothing for Noise Reduction in Very Dim Light Image Sequences
Author :
Malm, Henrik ; Warrant, Eric
Author_Institution :
Dept. of Cell & Organism Biol., Lund Univ.
Volume :
3
fYear :
0
fDate :
0-0 0
Firstpage :
954
Lastpage :
959
Abstract :
A new method for noise reduction using spatiotemporal smoothing is presented in this paper. The method is developed especially for reducing the noise that arises when acquiring video sequences with a camera under very dim light conditions. The work is inspired by research on the vision of nocturnal animals and the adaptive spatial and temporal summation that is prevalent in the visual systems of these animals. From analysis using the so-called structure tensor in the three-dimensional spatiotemporal space, motion segmentation and global ego-motion estimation, Gaussian shaped smoothing kernels are oriented mainly in the direction of the motion and in spatially homogeneous directions. In static areas, smoothing along the temporal dimension is favoured for maximum preservation of structure. The technique has been applied to various dim light image sequences and results of these experiments are presented here
Keywords :
cameras; image denoising; image segmentation; image sequences; motion estimation; smoothing methods; video signal processing; zoology; Gaussian shaped smoothing kernels; camera; global ego-motion estimation; image sequences; motion dependent spatiotemporal smoothing; motion segmentation; nocturnal animals; noise reduction; structure tensor; video sequences; Animal structures; Cameras; Image sequences; Motion segmentation; Noise reduction; Smoothing methods; Spatiotemporal phenomena; Tensile stress; Video sequences; Visual system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location :
Hong Kong
ISSN :
1051-4651
Print_ISBN :
0-7695-2521-0
Type :
conf
DOI :
10.1109/ICPR.2006.813
Filename :
1699684
Link To Document :
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