Model selection criteria for image restoration

Authors:
Abd-Krim Seghouane
Affiliations:
Canberra Research Laboratory, National ICT Australia, Canberra, A.C.T., Australia and College of Engineering and Computer Science, The Australian National University, Canberra, A.C.T., Australia
Venue:
IEEE Transactions on Neural Networks
Year:
2009

Citing 9
Cited 0

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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 based on Bayesian arguments and the Kullback-Leibler divergence and they can be considered as extensions of the Bayesian information criterion (BIC) and the Akaike information criterion (AIC) for the image restoration problem.