Wavelet-based adaptive regularization deconvolution for turbulence-degraded image

摘要

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