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Deep Learning based Fourier Spectrum Sampling Strategy for Improving Performance of Imaging

EasyChair Preprint no. 1476

4 pagesDate: September 5, 2019


Single-pixel imaging is a framework that reconstructs the target information only with a single bucket detector. The principle of the single-pixel imaging is correlating the measurements of a single bucket detector and the corresponding 2D light field distributions modulated by an optical field device in the scene. Single-pixel imaging has a good prospect in various imaging applications. To improve the imaging quality and speed, the compressed sensing and the basis scan strategies are demonstrated at the current stage. Based on the Fourier single-pixel imaging, the representative one of the basis scan strategies, and the deep learning, we propose a deep learning based Fourier spectrum sampling strategy for Fourier single-pixel imaging. Our goal is to predict and acquire the significant Fourier coefficients instead of the traditional sampling strategies to recover higher quality image under the same measurements. The simulation results demonstrate that the reconstruction image of the proposed strategy outperforms others. Applications to high quality and speed imaging could benefit from our strategy.

Keyphrases: Convolutional Neural Network, deep learning, Fourier single-pixel imaging, single-pixel imaging

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Kaiyu Zhang and Jie Cao and Lei Yan and Fanghua Zhang and Qun Hao},
  title = {Deep Learning based Fourier Spectrum Sampling Strategy for Improving Performance of Imaging},
  howpublished = {EasyChair Preprint no. 1476},

  year = {EasyChair, 2019}}
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