DocumentCode :
578388
Title :
Iterative data adaptive anisotropic image filtering
Author :
Yang, Chang-cai ; Zheng, Xin-yi ; Tian, Jin-wen ; Shang, Ke ; Tian, Xin ; Hao, Wei
Author_Institution :
Sci. & Technol. on Multi-Spectral Inf. Process. Lab., Huazhong Univ. of Sci. & Technol., Wuhan, China
Volume :
3
fYear :
2012
fDate :
15-17 July 2012
Firstpage :
1169
Lastpage :
1174
Abstract :
This paper presents a novel data-adaptive anisotropic filtering technique built on top of an iterative scheme. This new technique can preserve the original significant structures while suppressing noises to the largest extent. To achieve this goal, dominant orientation information of all gradients is used to control the local kernel adaptively. This results in elongated and elliptical contours spread along the directions of the local edge structure. With these locally adapted kernels, the smoothing is mostly effective along the edges, rather than across them. Therefore details in the original input can be preserved. The performance of the proposed data-adaptive anisotropic filter together with its iterative scheme are compared with the conventional Gaussian filter, anisotropic diffusion filter, the structure-adaptive anisotropic filter, and the iterative kernel regression scheme. The experimental results on both simulated Gaussian and Poission noisy images indicate that the proposed method performs the best, and the iterative scheme can significantly improve the performance of the noise suppressing while delivering high computation efficiency.
Keywords :
edge detection; filtering theory; image denoising; iterative methods; performance evaluation; adaptive local kernel control; elliptical contours; elongated contours; gradient orientation information; iterative data adaptive anisotropic image filtering; local edge structure; locally adapted kernels; noises suppression; performance improvement; simulated Gaussian noisy images; simulated Poission noisy images; Abstracts; Noise; data-adaptive anisotropic filter; image processing; iterative image filtering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2012 International Conference on
Conference_Location :
Xian
ISSN :
2160-133X
Print_ISBN :
978-1-4673-1484-8
Type :
conf
DOI :
10.1109/ICMLC.2012.6359521
Filename :
6359521
Link To Document :
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