Wavelet-based adaptive regularization deconvolution for turbulence-degraded image

Bo Chen; Ze-xun Geng; Tian-Shuang Shen; Yang Yang

doi:10.1117/12.804855

2 February 2009 Wavelet-based adaptive regularization deconvolution for turbulence-degraded image

Bo Chen, Ze-xun Geng, Tian-Shuang Shen, Yang Yang

Proceedings Volume 7157, 2008 International Conference on Optical Instruments and Technology: Advanced Sensor Technologies and Applications; 71570I (2009) https://doi.org/10.1117/12.804855
Event: International Conference of Optical Instrument and Technology, 2008, Beijing, China

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.

Citation Download Citation

Bo Chen, Ze-xun Geng, Tian-Shuang Shen, and Yang Yang "Wavelet-based adaptive regularization deconvolution for turbulence-degraded image", Proc. SPIE 7157, 2008 International Conference on Optical Instruments and Technology: Advanced Sensor Technologies and Applications, 71570I (2 February 2009); https://doi.org/10.1117/12.804855

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KEYWORDS

Wavelets

Image restoration

Wavelet transforms

Deconvolution

Point spread functions

Linear filtering

Filtering (signal processing)

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