Anisotropic phase-map denoising using a regularized cost-function with complex-valued Markov-random-fields

In our recently reported work [1] (Villa et al., 2009) we derived a regularized quadratic-cost function, which includes fringe orientation information, for denosing fringe pattern images. In this work we adopt such idea for denoising wrapped phase-maps. We use a regularized cost-function that uses complex-valued Markov random fields (CMRFs) with orientation information of the filtering direction along isophase lines. The advantage of using an anisotropic filter along isophase lines is that phase and noise can be properly separated while 2π phase jumps are preserved even in high frequency zones. Apart from its robustness, the outstanding advantage of our method is its minimal computational effort. We present some results processing simulated and real phase-maps.