@article{c37c3c08-8c9b-49b1-8ada-289ca888d785,
  abstract     = {{<p>In situ particle characterization is crucial for experimentally evaluating multiphase flow systems, where digital holography offers unique three-dimensional diagnostics. However, reliable particle localization remains challenging under strong optical noise encountered in practical environments. This study introduces a similarity-driven autofocusing framework that reformulates depth sensing by leveraging inter-particle holographic similarity, quantified by mutual information (MI), as a robust, collective focus criterion. Through systematic benchmarking against seven conventional algorithms, MI demonstrates superior localization accuracy under diverse noise conditions (Gaussian, Speckle, and Salt and Pepper noise at signal-to-noise ratio ∼ 10 dB). The core innovation lies in the first application of MI to establish a noise-immune focus signature from correlated particle-pair propagation. Experimental validation on noisy holograms of combusting metal particles confirms the method's efficacy. This work bridges information theory and computational optics, establishing a new approach for reliable particle tracking in combustion diagnostics, aerospace propulsion, and industrial spray characterization.</p>}},
  author       = {{Huang, Jianqing and Cai, Weiwei and Li, Zhongshan and Huang, Yue}},
  issn         = {{1070-6631}},
  language     = {{eng}},
  month        = {{07}},
  number       = {{7}},
  publisher    = {{American Institute of Physics (AIP)}},
  series       = {{Physics of Fluids}},
  title        = {{Accurate particle three-dimensional diagnostic in noisy combustion environments through holographic similarity analysis}},
  url          = {{http://dx.doi.org/10.1063/5.0343481}},
  doi          = {{10.1063/5.0343481}},
  volume       = {{38}},
  year         = {{2026}},
}