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.
Surrogate-based cross-correlation for particle image velocimetry
School of Mechanical and Electronic Engineering, Wuhan University of Technology（武汉理工大学机械与电子工程学院）；College of Computer Science, Chongqing University（重庆大学计算机学院）；Huazhong University of Science and Technology（华中科技大学）
Compressive Online Robust Principal Component Analysis with Optical Flow for Video Foreground-Background Separation
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；University of Engineering and Technology, Vietnam National University, Hanoi（越南国立大学河内工程技术大学）
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset
Univ. La Rochelle（拉罗谢尔大学）；Kyungpook National University（庆北国立大学）
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing（空间智能信息处理国家重点实验室）；Department of Electrical and Computer Engineering, Mississippi State University（电气与计算机工程系，密苏里州立大学）；National Research Council, Institute of Methodologies for Environmental Analysis (CNR-IMAA)（国家研究理事会，环境分析方法研究所（CNR-IMAA））
Nuclear Diffusion Models for Low-Rank Background Suppression in Videos
Dept. of Electrical Engineering, Eindhoven University of Technology, the Netherlands（埃因霍温理工大学电气工程系，荷兰）；Philips Research North America, Cambridge MA, USA（飞利浦北美研究部，马萨诸塞州剑桥）
Outlier-aware Tensor Robust Principal Component Analysis with Self-guided Data Augmentation
School of Mathematics and Statistics, Hunan Normal University（湖南师范大学数学与统计学学院）；School of Statistics and Mathematics, Yunnan University of Finance and Economics（云南财经大学统计与数学学院）
School of Applied and Engineering Physics, Cornell University（康奈尔大学应用与工程物理学院）；Department of Physics, Cornell University（康奈尔大学物理系）；Kavli Institute at Cornell for Nanoscale Science（康奈尔大学卡夫利纳米科学研究所）
