A hybrid unsupervised learning approach for noise removal in particle image velocimetry

Particle image velocimetry technology calculates velocity fields by capturing two consecutive frames of particle images, and it is widely used in the research of fluid mechanics, meteorology, aerospace, and other fields. Challenges arise from uncontrolled refraction and reflection of lasers in water, as well as reflections from moving objects in water, introducing noise in particle images. Changes in noise shape and its inconsistent motion direction with particles can affect the accuracy of velocity field results. A particular challenge addressed in this study is the removal of noise from particle images. Existing learning-based methods employ supervised models, often relying on synthetic datasets due to the difficulty in obtaining pairs of particle images (noisy images and corresponding noise-free images) for training, leading to a gap between training setups and real-world scenarios. In this paper, a hybrid model named dynamic partition histogram matching with PatchCore (DPHMP) is proposed. This model comprises two primary steps. Initially, it detects noise in particle images utilizing PatchCore, which establishes a memory bank for identifying noisy regions. Subsequently, noise removal is achieved through dynamic partition histogram matching. To validate the effectiveness DPHMP, a semi-synthetic dataset and a real dataset are generated containing real particle images with real noise. On the semi-synthetic dataset, the DPHMP method achieves a peak signal-to-noise ratio of 34.393 and a structural similarity index measure of 0.9722 between the denoised and real noise-free particle images, outperforming all existing methods. Moreover, on real datasets, the approach also surpasses other techniques.