Motion dependent spatiotemporal smoothing for noise reduction in very dim light image sequences
Malm, Henrik LU and Warrant, Eric LU
(2006)
18th International Conference on Pattern Recognition, ICPR 2006
3.
p.954-959
- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/616788
- author
- Malm, Henrik
LU
and Warrant, Eric
LU
- organization
- publishing date
- 2006
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Visual systems, Gaussian shaped smoothing kernels, Video sequences, Image sequences
- host publication
- Proceedings - International Conference on Pattern Recognition
- volume
- 3
- pages
- 954 - 959
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 18th International Conference on Pattern Recognition, ICPR 2006
- conference location
- Hong Kong, China
- conference dates
- 2006-08-20 - 2006-08-24
- external identifiers
-
- wos:000240705600230
- scopus:34147142880
- ISSN
- 1051-4651
- DOI
- 10.1109/ICPR.2006.813
- language
- English
- LU publication?
- yes
- id
- 8bf3b842-5c77-4005-b088-b4bb4ddd04f5 (old id 616788)
- date added to LUP
- 2016-04-01 16:14:15
- date last changed
- 2024-01-11 04:20:49
@inproceedings{8bf3b842-5c77-4005-b088-b4bb4ddd04f5,
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.}},
author = {{Malm, Henrik and Warrant, Eric}},
booktitle = {{Proceedings - International Conference on Pattern Recognition}},
issn = {{1051-4651}},
keywords = {{Visual systems; Gaussian shaped smoothing kernels; Video sequences; Image sequences}},
language = {{eng}},
pages = {{954--959}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Motion dependent spatiotemporal smoothing for noise reduction in very dim light image sequences}},
url = {{http://dx.doi.org/10.1109/ICPR.2006.813}},
doi = {{10.1109/ICPR.2006.813}},
volume = {{3}},
year = {{2006}},
}