Iterative Restoration Methods to Loose Estimations Dependency of Regularized Solutions

Image restoration is a classical area of digital image processing, appearing in many applications such as remote sensing, medical imaging, astronomy or computerized tomography (González & Woods, 2007). Simply put, the aim is to recover an original image which has been degraded due to the imperfections in the acquisition system: blurring and noise. Restoring this degradation leads to an ill-posed problem since the simple inverse using least-squares yields highly noise-sensitive solutions. A large number of techniques have been developed to cope with this issue, most of them under the regularization or the Bayesian frameworks (a complete review can found in Banham & Katsaggelos, 1997; Bovik, 2005; Chan & Shen, 2005).