High topographical accuracy by optical shot noise reduction in digital holographic microscopy.

In this work, we present a new method to reduce the shot noise in phase imaging of digital holograms. A spatial averaging process of phase images reconstructed at different reconstruction distances is performed, with the reconstruction distance range being specified by the numerical focus depth of the optical system. An improved phase image is attained with a 50% shot noise reduction.

Design of a medical and laboratory equipment management program for the new standards certification achievement in Mexico.

Certification for healthcare institutions in Mexico is ruled by 2009 standards homologated with the Joint Commission International criteria. Nowadays, healthcare requires of medical equipment and devices, so it has become necessary to implement guidelines for its adequate management in order to reach the highest level of quality and safety at the lowest cost. The objective of this work was to develop a Medical and Laboratory Equipment Management Program, oriented to the improvement of quality, effectiveness and efficiency of the technological resources in order to meet the certification requirements.

Dynamic 3-D shape measurement method based on quadrature transform.

In this work, it is presented a combination of temporal phase unwrapping technique and Fourier-based quadrature transform to obtain the dynamic phase map from a vibrating object. The proposed combination results on a very simple algorithm which allows an accurate and versatile 3D reconstruction of the object under analysis.

Phase unwrapping through demodulation by use of the regularized phase-tracking technique.

Most interferogram demodulation techniques give the detected phase wrapped owing to the arctangent function involved in the final step of the demodulation process. To obtain a continuous detected phase, an unwrapping process must be performed. Here we propose a phase-unwrapping technique based on a regularized phase-tracking (RPT) system.

Regularization methods for processing fringe-pattern images.

A powerful technique for processing fringe-pattern images is based on Bayesian estimation theory with prior Markov random-field models. In this approach the solution of a processing problem is characterized as the minimizer of a cost function with terms that specify that the solution should be compatible with the available observations and terms that impose certain (prior) constraints on the solution. We show that, by the appropriate choice of these terms, one can use this approach in almost every processing step for accurate and robust interferogram demodulation and phase unwrapping.