  We introduce a novel end-to-end approach to improving the resolution of PIV
measurements. The method blends information from different snapshots without
the need for time-resolved measurements on grounds of similarity of flow
regions in different snapshots. The main hypothesis is that, with a
sufficiently large ensemble of statistically-independent snapshots, the
identification of flow structures that are morphologically similar but
occurring at different time instants is feasible. Measured individual vectors
from different snapshots with similar flow organisation can thus be merged,
resulting in an artificially increased particle concentration. This allows to
refine the interrogation region and, consequently, increase the spatial
resolution. The measurement domain is split in subdomains. The similarity is
enforced only on a local scale, i.e. morphologically-similar regions are sought
only among subdomains corresponding to the same flow region. The identification
of locally-similar snapshots is based on unsupervised K-nearest neighbours
search in a space of significant flow features. Such features are defined in
terms of a Proper Orthogonal Decomposition, performed in subdomains on the
original low-resolution data, obtained either with standard cross-correlation
or with binning of Particle Tracking Velocimetry data with a relatively large
bin size. A refined bin size is then selected according to the number of
"sufficiently close" snapshots identified. The statistical dispersion of the
velocity vectors within the bin is then used to estimate the uncertainty and to
select the optimal K which minimises it. The method is tested and validated
against datasets with a progressively increasing level of complexity: two
virtual experiments based on direct simulations of the wake of a fluidic
pinball and a channel flow and the experimental data collected in a turbulent
boundary layer.
