Robust Principal Component Analysis for Background Estimation of Particle Image Velocimetry Data
This addresses background estimation issues in PIV data processing for fluid dynamics researchers, but it is incremental as it applies an existing method (RPCA) to a specific domain.
The paper tackled the problem of artifacts like light reflections and backgrounds affecting Particle Image Velocimetry (PIV) data processing by proposing a novel approach using Robust Principal Component Analysis (RPCA) to decompose data into background and foreground components, which experiments showed to be superior to state-of-the-art methods for background removal.
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.