100 kHz CH2O imaging realized by lower speed planar laser-induced fluorescence and deep learning

Wei Zhang, Xue Dong, Zhiwei Sun, Bo Zhou, Zhenkan Wang, Mattias Richter

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review

1 Citering (SciVal)

Sammanfattning

This paper reports an approach to interpolate planar laser-induced fluorescence (PLIF) images of CH2O between consecutive experimental data by means of computational imaging realized with convolutional neural network (CNN). Such a deep learning based method can achieve higher temporal resolution for 2D visualization of intermediate species in combustion based on high-speed experimental images. The capability of the model was tested for generating 100 kHz PLIF images by interpolating single and multiple PLIF frames into the sequences of experimental images of lower frequencies (50, 33, 25 and 20 kHz). Results show that the prediction indices, including intersection over union (IoU), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and time averaged correlation coefficient at various axial positions could achieve acceptable accuracy. This work sheds light on the utilization of CNN-based models to achieve optical flow computation and image sequence interpolation, also providing an efficient off-line model as an alternative pathway to overcome the experimental challenges of the state-of-the-art ultra-high speed PLIF techniques, e.g., to further increase repetition rate and save data transfer time.

Originalspråkengelska
Sidor (från-till)30857-30877
Antal sidor21
TidskriftOptics Express
Volym29
Utgåva19
DOI
StatusPublished - 2021 sep. 13

Ämnesklassifikation (UKÄ)

  • Atom- och molekylfysik och optik
  • Energiteknik

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