A variational Bayesian method to inverse problems with impulsive noise
Journal of Computational Physics
High accuracy TOF and stereo sensor fusion at interactive rates
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Proximity algorithms for the L1/TV image denoising model
Advances in Computational Mathematics
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A novel splitting method is presented for the $\ell^1$-$TV$ restoration of degraded images subject to impulsive noise. The functional is split into an $\ell^2$-$TV$ denoising part and an $\ell^1$-$\ell^2$ deblurring part. The dual problem of the relaxed functional is smooth with convex constraints and can be solved efficiently by applying an Arrow-Hurwicz-type algorithm to the augmented Lagrangian formulation. The regularization parameter is chosen automatically based on a balancing principle. The accuracy, the fast convergence, and the robustness of the algorithm and the use of the parameter choice rule are illustrated on some benchmark images and compared with an existing method.