Fast estimation of modulo 2pi fringe orientation

A regularized estimator for modulo 2p fringe orientation is presented in this work. As the technique requires to solve locally in the fringe pattern a simple linear system to optimize a regularized cost function, the global estimation of an orientation vector field is performed fast and easily. The performance of this technique is evaluated with synthetic and real fringe patterns

Bayesian entropy estimation applied to non-gaussian robust image segmentation

We introduce a new approach for robust image segmentation combining two strategies within a Bayesian framework. The first one is to use a Markov random field (MRF) which allows to introduce prior information with the purpose of image edges preservation. The second strategy comes from the fact that the probability density function (pdf) of the likelihood function is non-Gaussian or unknown, so it should be approximated by an estimated version, which is obtained by using the classical non-parametric or kernel density estimation. This lead us to the definition of a new maximum a posteriori (MAP) estimator based on the minimization of the entropy of the estimated pdf of the likelihood function and the MRF at the same time, named MAP entropy estimator (MAPEE). Some experiments were made for different kind of images degraded with impulsive noise (salt & pepper) and the segmentation results are very satisfactory and promising

An alternative differential method of femtosecond pump-probe examination of materials

We describe an alternative method for femtosecond pumpprobe beam examination of energy transport properties of materials. All already reported techniques have several drawbacks which limit precise measurements of reflection coefficient as function of time. A typical problem is present when rough samples are being studied. In this case the pump-beam polarization changes randomly which may produce a spurious signal, drastically reducing the signal to noise ratio. Some proposals to alleviate such problema have been reported, however, they have not been totally satisfactory. The method presented here consists on measuring the difference between the two delays’ signals of the probe-beam. As will be explained, our proposal is free of typical drawbacks. We also propose a numerical method to recover the DR(t)/R curve from the measured data. Numerical simulations show that our proposal is a viable alternative

Regularized quadratic cost function for oriented fringe-pattern filtering

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

New approach of entropy estimation for robust image segmentation

In this work we introduce a new approach for robust image segmentation. The idea is to combine two strategies within a Bayesian framework. The first one is to use a Márkov Random Field (MRF), which allows to introduce prior information with the purpose of preserve the edges in the image. The second strategy comes from the fact that the probability density function (pdf) of the likelihood function is non Gaussian or unknown, so it should be approximated by an estimated version, and for this, it is used the classical non-parametric or kernel density estimation. This two strategies together lead us to the definition of a new maximum a posteriori (MAP) estimator based on the minimization of the entropy of the estimated pdf of the likelihood function and the MRF at the same time, named MAP entropy estimator (MAPEE). Some experiments were made for different kind of images degraded with impulsive noise and the segmentation results are very satisfactory and promising

Funciones Radiales de Base para Desenvolvimiento de Fase

An important step in fringe pattern analysis is the so called phase unwrapping. Although this task can be performed easily using path dependent algorithms, most times, however, these algorithms are not robust enough specially in the presence of noise. On the other hand, path independent methods such as least-squares based or regularization based may be little convenient due to programming complexity or time consuming. In this paper we describe an alternative algorithm for phase unwrapping based in the determination of weights to linearly combine a set of radial basis functions (RBFs). As described, our algorithm is fast and can be easily implemented following a simple matrix formulation. Numerical and real experiments with good results show that our method can be applied in many kinds of optical tests.Un importante paso en el análisis de patrones de franjas es el llamado desenvolvimiento de fase. Aunque esta tarea puede ser realizada fácilmente usando algoritmos dependientes del camino, muchas veces, sin embargo, estos algoritmos no son suficientemente robustos especialmente con la presencia de ruido. Por otro lado, los métodos independientes del camino tales como los basados en mínimos cuadrados o regularización pueden ser poco convenientes debido a la complejidad de programación o al tiempo de procesado. En este artículo describimos un algoritmo alternativo para desenvolvimiento de fase basado en la determinación de pesos para combinar linealmente un conjunto de funciones radiales de base (FRBs). Como se describe, nuestro algoritmo es rápido y puede ser fácilmente implementado siguiendo una formulación matricial simple. Experimentos numéricos y reales con buenos resultados muestran que nuestro método puede ser aplicado a muchos de los tipos de pruebas ópticas

Funciones Radiales de Base para Desenvolvimiento de Fase

An important step in fringe pattern analysis is the so called phase unwrapping. Although this task can be performed easily using path dependent algorithms, most times, however, these algorithms are not robust enough specially in the presence of noise. On the other hand, path independent methods such as least-squares based or regularization based may be little convenient due to programming complexity or time consuming. In this paper we describe an alternative algorithm for phase unwrapping based in the determination of weights to linearly combine a set of radial basis functions (RBFs). As described, our algorithm is fast and can be easily implemented following a simple matrix formulation. Numerical and real experiments with good results show that our method can be applied in many kinds of optical tests.Un importante paso en el análisis de patrones de franjas es el llamado desenvolvimiento de fase. Aunque esta tarea puede ser realizada fácilmente usando algoritmos dependientes del camino, muchas veces, sin embargo, estos algoritmos no son suficientemente robustos especialmente con la presencia de ruido. Por otro lado, los métodos independientes del camino tales como los basados en mínimos cuadrados o regularización pueden ser poco convenientes debido a la complejidad de programación o al tiempo de procesado. En este artículo describimos un algoritmo alternativo para desenvolvimiento de fase basado en la determinación de pesos para combinar linealmente un conjunto de funciones radiales de base (FRBs). Como se describe, nuestro algoritmo es rápido y puede ser fácilmente implementado siguiendo una formulación matricial simple. Experimentos numéricos y reales con buenos resultados muestran que nuestro método puede ser aplicado a muchos de los tipos de pruebas ópticas

Image restoration a comparative study of some methods applied to color images

The present work introduces five different methods to deal with digital image restoration. Particularly deconvolution by Richardson-Lucy method, Wiener filter, deconvolution with Gaussian priors in the frequency domain, spatial domain and the use of sparse priors. The Bayesian methodology is based on the prior knowledge of some information that allows an efficient modeling of the image acquisition process. The edge preservation of objects into the image while smoothing noise is necessary for an adequate model. Thus, we use five deconvolution methods to recover images, all of the presented images are contained on TID 2008, all of them were previously degraded by Gaussian noise and convolved with a disc point spread function (PSF) making reference to a typical fluorescence microscopy degradation. The principal objective when using restoration methods in the context of image processing is to eliminate those effects caused by the excessive smoothness on the reconstruction process of an image which is rich in contours or edges and also is important to consider the process time due to an improvement in this área could lead to a faster application. A comparison between the five methods is presented for a restoration process. This collection of implemented methods has been compared using different metrics such as SNR, PSNR, SSIM and process time. The obtained results showed a satisfactory performance and the effectiveness of the proposed methods on color space