% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Lagemann:271108,
      author       = {Lagemann, Christian and Lagemann, Kai and Mukherjee, Sach
                      and Schröder, Wolfgang},
      title        = {{C}hallenges of deep unsupervised optical flow estimation
                      for particle-image velocimetry data},
      journal      = {Experiments in fluids},
      volume       = {65},
      number       = {3},
      issn         = {0723-4864},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer},
      reportid     = {DZNE-2024-00980},
      pages        = {30},
      year         = {2024},
      abstract     = {In recent years, several algorithms have been proposed that
                      leverage deep learning techniques within the analysis
                      workflow of particle-image velocimetry (PIV) measurements.
                      This emerging body of work has shown that deep learning has
                      the potential to match or outperform state-of-the-art
                      classical algorithms in terms of efficiency, accuracy, and
                      spatial resolution. However, the huge diversity in dynamic
                      flows and varying particle-image conditions require PIV
                      processing schemes to have high generalization capabilities
                      to unseen flow and lighting conditions. If these conditions
                      vary strongly compared to the training data, the performance
                      of fully supervised PIV tools can degrade substantially. In
                      contrast, unsupervised learning ameliorates the need for
                      comprehensive labeled training data and can permit a much
                      wider range of data to be used during training. Therefore,
                      unsupervised deep learning could improve inference
                      capability for challenging real-world use cases. However,
                      design of an unsupervised loss objective is non-trivial and
                      requires application-specific consideration. Motivated by
                      the foregoing, in this paper we study unsupervised deep
                      learning for PIV processing, systematically investigating
                      key components of losses and accommodating regularizers and
                      deriving a proxy loss. The resulting algorithm, named
                      Unsupervised Recurrent All-Pairs Field Transforms for PIV
                      (URAFT-PIV), is unsupervised and meant specifically for PIV
                      applications. We investigate performance under varying image
                      and lighting conditions in synthetic and experimental data,
                      with a breadth and depth going well beyond currently
                      available empirical results. These results shed new light on
                      deep learning for PIV processing and in particular on the
                      scope for unsupervised learning in this domain.},
      cin          = {AG Mukherjee},
      ddc          = {530},
      cid          = {I:(DE-2719)1013030},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1007/s00348-024-03768-2},
      url          = {https://pub.dzne.de/record/271108},
}