Bayes Information Criterion for Tikhonov Problems with Linear Constraints: Application to Radiometric Image Correction

P. Carvalho, A. Santos, A. Dourado, and B. Ribeiro (Portugal)


Machine Learning, Image Correction, Camera Calibration.


Ill-conditioned or singular data modeling problems are commonly observed in image processing. To solve these problems some constraints, such as smoothness and bound ary conditions have to be formulated. Further, the opti mal structure of the model is not always self-evident. Most selection criteria (i) are not appropriate for problems with linear constraints and, further (ii) are usually not simultane ously suitable for model and regularization gain selection. In this paper the Bayes Information Criterion is extended for Tikhonov problems with linear constraints. Using this measure, a new radiometric image correction method is in troduced. All known radiometric correction algorithms as sume that radiometric distortions remain stable over time. Our algorithm enables image correction under time vary ing distortions. The method decomposes radiometric image distortions into multiplicative and additive errors, whose optimal models are computed with the extended Bayes In formation Criterion (BICIC).

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