Convolutional neural network optimisation to enhance ESPI fringe visibility

Abstract

The use of convolutional neuronal networks (CNN) for the treatment of interferometric fringes has been introduced in recent years. In this paper, we optimize and build a CNN model, based U-NET architecture, to maximize its performance processing electronic speckle interferometry fringes (ESPI). The proposed approach is based on quick and light trainings to select the architecture parameters (network depth and kernel sizes) to maximize the performance of the neural network improving the visibility of ESPI images. To measure the performance, the structural similarity index (SSMI) will be the lead indicator, and the need for large datasets to train neural networks, unavailable for ESPI images, forces the use of a simulated ESPI image dataset along the process. This dataset is computed using Zernike polynomials to simulate local surface deformations in the specimen under test and simulated true speckle fields for the reference and object field involved in ESPI techniques.