Robust Principal Component Analysis for Background Estimation of Particle Image Velocimetry Data

TL;DR

This paper introduces a robust method using Principal Component Analysis to effectively separate background and moving particles in Particle Image Velocimetry data, improving artifact reduction.

Contribution

It applies Robust Principal Component Analysis with an optimization approach to enhance background estimation in PIV data, addressing artifacts more effectively than existing methods.

Findings

Superior background removal in PIV data demonstrated

Effective separation of background and particles achieved

Outperforms state-of-the-art techniques

Abstract

Particle Image Velocimetry (PIV) data processing procedures are adversely affected by light reflections and backgrounds as well as defects in the models and sticky particles that occlude the inner walls of the boundaries. In this paper, a novel approach is proposed for decomposition of the PIV data into background/foreground components, greatly reducing the effects of such artifacts. This is achieved by utilizing Robust Principal Component Analysis (RPCA) applied to the data matrix, generated by aggregating the vectorized PIV frames. It is assumed that the data matrix can be decomposed into two statistically different components, a low-rank component depicting the still background and a sparse component representing the moving particles within the imaged geometry. Formulating the assumptions as an optimization problem, Augmented Lagrange Multiplier (ALM) method is used for decomposing the data matrix into the low-rank and sparse components. Experiments and comparisons with the state-of-the-art using several PIV image sequences reveal the superiority of the proposed approach for background removal of PIV data.