Extrinsic crosstalk suppression based on a calibration convolutional neural network for glasses-free 3D display

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Extrinsic crosstalk degrades the 3D imaging quality and it occurs due to fabrication errors of the light control optical component and assembling errors of the display system. Here, an extrinsic crosstalk suppression method based on a calibration convolutional neural network (C-CNN) is demonstrated for glasses-free 3D display. The C-CNN is established to calibrate the 3D imaging with extrinsic crosstalk, which can obtain the high-order nonlinear decoding function of the light control optical component with fabrication errors and assembling errors. Furthermore, model for 3D imaging with both extrinsic and intrinsic crosstalk can be mathematically established using the C-CNN integrated with the frequency response calculation. Additionally, according to the calibration for real-life 3D imaging with extrinsic crosstalk, pixels of elemental image array (EIA) contributing to viewpoint crosstalk are extinguished to suppress extrinsic crosstalk. The experiment verifies the feasibility and superior of the proposed method. A 5-degree viewing angle and 5 viewpoints glasses-free 3D display with improved 3D imaging quality is demonstrated.