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.
© 2009 Optical Society of America
Original Manuscript: March 10, 2009
Revised Manuscript: April 20, 2009
Manuscript Accepted: April 27, 2009
Published: May 29, 2009
Jesús Villa, Juan Antonio Quiroga, and Ismael De la Rosa, "Regularized quadratic cost function for oriented fringe-pattern filtering," Opt. Lett. 34, 1741-1743 (2009)