DeepVID: deep-learning accelerated variational image decomposition model tailored to fringe pattern filtration

Abstract

Abstract The algorithms defined as variational image decomposition (VID) constitute the state-of-the-art in solving the image prefiltration problem. However, the discussion about the advantages and disadvantages of different VID models in the context of fringe pattern prefiltration is yet to be addressed and this work is the first one leaning into this issue. The unsupervised variational image decomposition (uVID) algorithm allows for automatic, accurate and robust preprocessing of diverse fringe patterns and introduces the parameters and stopping criterion for Chambolle’s iterative projection algorithm to separate the fringes and background. However, determining the stopping criterion in each iteration is a severely time-consuming process, which is particularly important given the fact that in many cases thousands of iterations must be calculated to obtain a satisfactory fringe pattern decomposition result. Therefore, the idea of using convolutional neural network to map the relationship between the fringe pattern spatial intensity distribution and the required number of Chambolle projection iterations has emerged. That way, it is no longer required to determine the value of the stopping criterion in every iteration, but the appropriate number of iterations is known in advance via machine learning process. We showed that the calculation time is reduced on average by 3–4 times by employing the deep learning-based acceleration (convolutional neural network called DeepVID) without jeopardizing the overall accuracy of the prefiltration. This way an important progress in developing uVID algorithm features towards real-time studies of dynamic phenomena is reported in this contribution. For the sake of metrological figure of merit, we employ deep learning based solution, for the first time to the best of our knowledge, to accelerate powerful and well-established VID approach, not to bypass it completely.