Holography has been widely used in optical displays, high-security optical encryption, and optical artificial intelligence. Optical multiplexing technologies by utilizing various dimensions of light effectively expand the information capacity and density for holography. In this work, we propose and experimentally demonstrate a novel spatially structured-mode multiplexing holography with the assistance of deep learning algorithms. In the experiment, we utilize Hermite−Gaussian (HG) and Laguerre−Gaussian (LG) modes for example as decoding channels of various holographic images. The results prove that these spatial modes work well as a multiplexing dimension in addition to wavelength, polarization, and orbital angular momentum (OAM) of light. In addition, by designing a specifically computed hologram, multiple spatial modes can be superposed together to compose a single decoding channel, which can significantly enhance the capacity and security for holographic encryption. Our work provides a promising scheme for high-capacity computational holography and information encryption.

It is a crucial task to capture clear pictures of fast-rotating objects for both scientific research and industrial manufacture. However, when the rotational speed exceeds the camera response, the recorded image generally suffers from motion blur. Particularly, the rotation-induced blur becomes severer along with the increasing radius. It still lacks an effective way to break the camera limitation and restore the image from rotational motion blur. Here, we propose and experimentally demonstrate a novel Laguerre–Gaussian (LG) domain rotational image restoration technique. In LG domain, the rotation introduces an additional phase into each azimuthal LG component of the image, resulting in motion blur. The blur factor of LG spectrum is related to the rotational speed, which can be precisely measured according to the rotational Doppler effect. After correcting the LG spectrum, we successfully recover the image of an object rotating at a constant or time-varying speed beyond the camera response. Our results provide a useful approach to record high-quality images of astronomical targets, biological molecules, and industrial centrifuges rotating at an ultrahigh speed.

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