RESUMEN
When experimental photoelasticity images are acquired, the spectral interaction between the light source and the sensor used affect the visual information of the fringe patterns in the produced images. Such interaction can lead to fringe patterns with an overall high quality, but also can lead to images with indistinguishable fringes, and bad stress field reconstruction. We introduce a strategy to evaluate such interaction that relies on measuring the value of four handcrafted descriptors: contrast, an image descriptor that accounts simultaneously for blur and noise, a Fourier-based descriptor to measure image quality, and image entropy. The utility of the proposed strategy was validated by measuring the selected descriptors on computational photoelasticity images, and the fringe orders achieved when evaluating the stress field, from 240 spectral configurations: 24 light sources and 10 sensors. We found that high values of the selected descriptors can be related to spectral configurations that lead to better stress field reconstruction. Overall, the results show that the selected descriptors can be useful to identify bad and good spectral interactions, which could help to design better protocols for acquiring photoelasticity images.
RESUMEN
Quantifying the stress field induced into a piece when it is loaded is important for engineering areas since it allows the possibility to characterize mechanical behaviors and fails caused by stress. For this task, digital photoelasticity has been highlighted by its visual capability of representing the stress information through images with isochromatic fringe patterns. Unfortunately, demodulating such fringes remains a complicated process that, in some cases, depends on several acquisitions, e.g., pixel-by-pixel comparisons, dynamic conditions of load applications, inconsistence corrections, dependence of users, fringe unwrapping processes, etc. Under these drawbacks and taking advantage of the power results reported on deep learning, such as the fringe unwrapping process, this paper develops a deep convolutional neural network for recovering the stress field wrapped into color fringe patterns acquired through digital photoelasticity studies. Our model relies on an untrained convolutional neural network to accurately demodulate the stress maps by inputting only one single photoelasticity image. We demonstrate that the proposed method faithfully recovers the stress field of complex fringe distributions on simulated images with an averaged performance of 92.41% according to the SSIM metric. With this, experimental cases of a disk and ring under compression were evaluated, achieving an averaged performance of 85% in the SSIM metric. These results, on the one hand, are in concordance with new tendencies in the optic community to deal with complicated problems through machine-learning strategies; on the other hand, it creates a new perspective in digital photoelasticity toward demodulating the stress field for a wider quantity of fringe distributions by requiring one single acquisition.
RESUMEN
We present a simultaneous dual-wavelength phase-imaging digital holographic technique demonstrated on porous coal samples. The use of two wavelengths enables us to increase the axial range at which the unambiguous phase imaging can be performed, but also increases the noise. We employ a noise reduction "fine map" algorithm, which uses the two-wavelength phase map as a guide to correct a single-wavelength phase image. Then, the resulting noise of a fine map is reduced to the level of single-wavelength noise. A comparison to software unwrapping is also presented. A simple way of correcting a curvature mismatch between the reference and the object beams is offered.