Denoising image-based experimental data without clean targets based on deep autoencoders

Abstract

Linear data-driven methods have demonstrated being effective denoising filters for image-based techniques. On the downside, they are still challenged by conditions of extremely low signal-to-noise ratio. A procedure based on denoising autoencoder (AE) is proposed here. The main challenge is the absence of a “clean” target, thus the training is referred to as “blind”. The method is validated on two synthetic datasets, based on the flow around a circular cylinder, and a fabricated pattern. The reconstruction accuracy is found to be mostly sensitive to the size of the latent space of the AE. A criterion to determine the optimal latent vector size based solely on the information available during the training process is proposed. The reconstruction error is shown to be up to one order of magnitude smaller than the case of a denoising Proper Orthogonal Decomposition. Furthermore, a fast approach using an attention mechanism is proposed. This fast alternative algorithm saves up to 98% training time with only a limited reconstruction accuracy loss if compared to the original approach. Finally, the superiority of the proposed AE blind denoising over linear data-driven filtering has been proved on Pressure Sensitive Paint data of an inclined impinging jet, showing a better performance than the POD denoising.