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We propose a new framework for processing Fringe Patterns (FP). Our novelapproach builds upon the hypothesis that the denoising and normalisation of FPscan be learned by a deep neural network if enough pairs of corrupted and idealFPs are provided. The main contributions of this paper are the following: (1)We propose the use of the U-net neural network architecture for FPnormalisation tasks; (2) we propose a modification for the distribution ofweights in the U-net, called here the V-net model, which is more convenient forreconstruction tasks, and we conduct extensive experimental evidence in whichthe V-net produces high-quality results for FP filtering and normalisation. (3)We also propose two modifications of the V-net scheme, namely, a residualversion called ResV-net and a fast operating version of the V-net, to evaluatethe potential improvements when modify our proposal. We evaluate theperformance of our methods in various scenarios: FPs corrupted with differentdegrees of noise, and corrupted with different noise distributions. We compareour methodology versus other state-of-the-art methods. The experimental results(on both synthetic and real data) demonstrate the capabilities and potential ofthis new paradigm for processing interferograms.

Deep neural network for fringe pattern filtering and normalisation