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Particle Image Velocimetry (PIV) data processing procedures are adverselyaffected by light reflections and backgrounds as well as defects in the modelsand sticky particles that occlude the inner walls of the boundaries. In thispaper, a novel approach is proposed for decomposition of the PIV data intobackground/foreground components, greatly reducing the effects of suchartifacts. This is achieved by utilizing Robust Principal Component Analysis(RPCA) applied to the data matrix, generated by aggregating the vectorized PIVframes. It is assumed that the data matrix can be decomposed into twostatistically different components, a low-rank component depicting the stillbackground and a sparse component representing the moving particles within theimaged geometry. Formulating the assumptions as an optimization problem,Augmented Lagrange Multiplier (ALM) method is used for decomposing the datamatrix into the low-rank and sparse components. Experiments and comparisonswith the state-of-the-art using several PIV image sequences reveal thesuperiority of the proposed approach for background removal of PIV data.

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