Iteratively reweighted unidirectional variational model for stripe non-uniformity correction

In this paper, we propose an adaptive unidirectional variational nonuniformity correction algorithm for fixed-pattern noise removal. The proposed algorithm is based on a unidirectional variational sparse model that makes use of unidirectional characteristics of stripe nonuniformity noise. The iteratively reweighted least squares (IRLS) technique is introduced to optimize the proposed correction model, which makes the proposed algorithm easy to implement with existing conjugate gradient method without introducing additional variables and parameters. Moreover, we derive a formula to automatically update the regularization parameter from the images. Comparative experimental results on real infrared images indicate that the proposed method can remove the stripe nonuniformity noise effectively while maintaining more useful image details.