Villa Hernández, José de Jesús; Quiroga Mellado, Juan Antonio; De la Rosa Vargas, José Ismael
Resumen:
We use the regularization theory in a Bayesian framework to derive a quadratic cost function for denoising
fringe patterns. As prior constraints for the regularization problem, we propose a Markov random field
model that includes information about the fringe orientation. In our cost function the regularization term
imposes constraints to the solution (i.e., the filtered image) to be smooth only along the fringe’s tangent direction.
In this way as the fringe information and noise are conveniently separated in the frequency space,
our technique avoids blurring the fringes. The attractiveness of the proposed filtering method is that the
minimization of the cost function can be easily implemented using iterative methods. To show the performance
of the proposed technique we present some results obtained by processing simulated and real fringe
patterns.