Particle image velocimetry (PIV) technology, which performs the full-field velocity measurement on the laser plane, plays a crucial role in studying complex flow structures in multistage centrifugal pumps. In particle image cross-correlation analysis, the flow field could be corrupted with outliers due to the background Gaussian imaging noise, insufficient illumination caused by optical obstruction, and particle slip caused by centrifugal forces. In this study, we propose a patch-based flow field reconstruction (PFFR) method for PIV data of multistage centrifugal pumps. Since natural images contain a large number of mutually similar patches at different locations, the instantaneous PIV data with a symmetric property is segmented to multiple patches. The flow field reconstruction is achieved by low-rank sparse decomposition, which exploits the information about similar flow characteristics present in patches. Furthermore, we illustrated the proposed PFFR on a large eddy simulation vorticity field and experimental data of a multistage centrifugal pump to evaluate its effectiveness. We also performed the three other data analysis methods. The results show that the proposed PFFR has a strong reconstruction ability to improve data reliability for the instantaneous flow field with outliers. When the outliers account for 20% of the total flow vectors, the average normalized root-mean-square error of PFFR-reconstructed data is 0.143, which is lower than the three other data methods by 21.9%–48.1%. The structural similarity is 0.702, which is higher than the three other data methods by 2.1%–9%.