  We propose a noise reduction method for unsteady pressure-sensitive paint
(PSP) data based on modal expansion, the coefficients of which are determined
from time-series data at optimally placed points. In this study, the proper
orthogonal decomposition (POD) mode calculated from the time-series PSP data is
used as a modal basis. Based on the POD modes, the points that effectively
represent the features of the pressure distribution are optimally placed by the
sensor optimization technique. Then, the time-dependent coefficient vector of
the POD modes is determined by minimizing the difference between the
time-series pressure data and the reconstructed pressure at the optimal points.
Here, the coefficient vector is assumed to be a sparse vector. The advantage of
the proposed method is a self-contained method, while existing methods use
other data such as pressure tap data for the reduction of the noise. As a
demonstration, we applied the proposed method to the PSP data measuring the
K\'arm\'an vortex street behind a square cylinder. The reconstructed pressure
data is agreed very well with the pressures independently measured by pressure
transducers.
