Advanced Imaging, Volume. 2, Issue 5, 051004(2025)

DWS-Net: a depth-wise separable convolutional neural network for robust phase-only hologram encoding

Shu-Feng Lin, Jingwei Chen, Dayong Wang, Jie Zhao, Lu Rong, Yunxin Wang, Yu Zhao*, and Chao Ping Chen*
Figures & Tables(11)
Phase-only hologram generation strategy of (a) the traditional encoding methods, (b) the traditional CNN methods, and (c) the proposed DWS-Net encoding method.
Architecture diagram of the proposed DWS-Net for POH encoding, including the depth-wise convolution process of down-sampling (in blue color), point-wise convolution process of down-sampling (in magenta color), complex de-convolution process in up-sampling (in yellow color), introduced RM in up-sampling (in green color), and introduced CCAM in up-sampling (in red color).
Network and training strategy comparison of (a) the traditional methods and (b) the proposed method. The upper parts are the usage, and the lower parts are the training process.
Schematic diagram of the optical setup for the holographic display prototype.
Comparison of digital simulation (upper rows of each method) and optical experiment (lower rows of each method) of the POH reconstruction encoded by different networks at different wavelengths (blue: 473 nm, green: 532 nm, red: 660 nm) under a fixed distance (z=222 mm).
Comparison of digital simulation (left column of each image) and optical experiment (right column of each image) of full-color encoding on sparse images.
Comparison of digital simulation and optical experiment of the POH reconstruction encoded by different networks with reconstructed distances at 183, 198, and 213 mm in blue (λ=473 nm).
Average PSNR and SSIM values of POHs corresponding to the two set images in Figs. 5 and 7, which are encoded by different networks with different reconstructed distances and different wavelengths. The straight line in each group represents the average value of all wavelengths and all reconstructed distances and the variance of the PSNR and SSIM was calculated for the two images across all wavelengths and distances.
Simulation results of different encoding capacities on original holograms (the first and fourth rows), and holograms after digital filtering by removing three out of four (the second and fifth rows) and five out of six (the third and sixth rows) of the outer high frequencies, comparing by reconstructed with the original complex amplitude value (the second column), encoded by the DWS-Net (the third column), and the double-phase encoding method (the fourth column).
Average reconstruction (a) PSNR and (b) SSIM values corresponding to the two set images for the DWS-Net encoding the digitally filtered complex amplitude holograms in different reconstructed distances and different wavelengths.
  • Table 1. Comparison of Network Performance Metrics.

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    Table 1. Comparison of Network Performance Metrics.

     DWSCRM&CCAMNetwork parameterPSNR/Var.SSIM/Var.Time (ms)
    CCNN××129,12226.77 dB/6.000.75/0.00316.46
    CCNN+RM&CCAM×178,09827.76 dB/15.580.79/0.01358.44
    CCNN+DWSC×95,74628.43 dB/1.320.82/0.000414.27
    CCNN+RM&CCAM+DWSC (DWS-Net)144,72229.19 dB/0.790.83/0.000616.68
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Shu-Feng Lin, Jingwei Chen, Dayong Wang, Jie Zhao, Lu Rong, Yunxin Wang, Yu Zhao, Chao Ping Chen, "DWS-Net: a depth-wise separable convolutional neural network for robust phase-only hologram encoding," Adv. Imaging 2, 051004 (2025)

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Paper Information

Category: Research Article

Received: May. 27, 2025

Accepted: Aug. 14, 2025

Published Online: Sep. 23, 2025

The Author Email: Yu Zhao (zhaoyu@yzu.edu.cn), Chao Ping Chen (ccp@sjtu.edu.cn)

DOI:10.3788/AI.2025.10012

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