Model selection criteria for image restoration

Abstract

In this brief, the image restoration problem is approached as a learning system problem, in which a model is to be selected and parameters are estimated. Although the parameters which correspond to the restored image can easily be obtained, their quality depend heavily on a proper choice of the regularization parameter that controls the tradeoff between fidelity to the blurred noisy observed image and the smoothness of the restored image. By analogy between the model selection philosophy that constitutes a fundamental task in systems learning and the choice of the regularization parameter, two criteria are proposed in this brief for selecting the regularization parameter. These criteria are ..