Wavelet-based adaptive regularization deconvolution for turbulence-degraded image
Bo Chen, Zexun Geng, Tian-shuang Shen, Yang Yang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
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.