  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.
