Recoding double-phase holograms with the full convolutional neural network

Abstract

Herein, we proposed a method to recode double-phase holograms (DPHs) with a full convolutional neural network (FCN) for alleviating the fringes and spatial shifting noises in that. There are many fringes near the edge of the diffraction field due to the incomplete Fresnel zone plate, and the pixel-by-pixel encoding method of DPH will cause above fringes to appear in the reconstructed image of that as well. Besides, the spatial shifting noises generated during the conversion from complex-amplitude information to pure-phase information will also decrease the reconstruction quality and definition. Depending on the great nonlinear fitting ability of FCN, the proposed method can recode DPHs, alleviating the fringes and removing the spatial shifting noises effectively. Finally, the phase-only holograms with the resolution 2400 × 4096 have been generated in 0.06 s, which have a peak signal to noise ratio (PSNR) of 35.6 dB in the mean reconstruction quality. Optical results showed that compared with the conventional methods, the target images reconstructed by the proposed method have less noise and a higher display quality.