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Proceedings Paper

Wavelet-based adaptive regularization deconvolution for turbulence-degraded image
Author(s): Bo Chen; Ze-xun Geng; Tian-Shuang Shen; Yang Yang
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Paper Abstract

The observed object images are seriously blurred because of the influence of atmospheric turbulence. The deconvolution is required for object reconstruction from turbulence degraded images. The wavelet transform provides a multiresolution approach to image analysis and processing. We consider a wavelet-based adaptive edge-preserving regularization deconvolution (WbARD) scheme for image restoration problems. This is accomplished by first casting the classical image restoration problem into the wavelet domain. We consider the behavior of the blur operator in the atrous wavelet domain. Then, we are able to adapt quite easily to scale-varying and orientation-varying features in the image while simultaneously retaining the edge preservation properties of the regularization. Experimental results show that the WbARD algorithm produces good performance in comparison to standard direct restoration approaches for turbulencedegraded images.

Paper Details

Date Published: 3 February 2009
PDF: 11 pages
Proc. SPIE 7157, 2008 International Conference on Optical Instruments and Technology: Advanced Sensor Technologies and Applications, 71570I (3 February 2009); doi: 10.1117/12.804855
Show Author Affiliations
Bo Chen, Guilin Air Force Academy (China)
Zhengzhou Institute of Surveying and Mapping (China)
Ze-xun Geng, Zhengzhou Institute of Surveying and Mapping (China)
Tian-Shuang Shen, Guilin Air Force Academy (China)
Yang Yang, Guilin Air Force Academy (China)


Published in SPIE Proceedings Vol. 7157:
2008 International Conference on Optical Instruments and Technology: Advanced Sensor Technologies and Applications

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