Under the framework of computational integral imaging, a multi-image encryption scheme based on the DNA-chaos algorithm is proposed. In this scheme, multiple images are merged to one image by a computational integral imaging algorithm, which significantly improves the efficiency of image encryption. Meanwhile, the computational integral imaging algorithm can merge images at different depth distances, thereby the different depth distances of multiple images can also be used as keys to increase the security of the encryption method. In addition, the high randomness of the chaos algorithm is combined to address the outline effect caused by the DNA encryption algorithm. We have experimentally verified the proposed multi-image encryption scheme. The entropy value of the encrypted image is 7.6227, whereas the entropy value of the merge image with two input images is 3.2886, which greatly reduces the relevance of the image. The simulation results also confirm that the proposed encryption scheme has high key security and can protect against various attacks.

The real-time performance of light-field 3D encryption technology based on the integral imaging principle is restricted by the acquisition speed and the data of the elemental image array (EIA). Herein, we propose a light-field 3D encryption scheme based on monocular depth rendering. With the help of a convolution residuals network (CRN), the proposed scheme can generate the corresponding depth map from a single RGB image and simplify the pickup process of the EIA according to the image mapping. For encryption, using reversible state loop cellular automata (RSL-CA) to encrypt a single RGB image updates traditional 3D encryption, greatly improving the security and efficiency of the encryption algorithm. It is experimentally demonstrated that optical 3D reconstruction is clear and brightly colorful and also has a good parallax effect. The proposed method can open a brand-new research perspective for light-field 3D encryption.

Color three-dimensional (3D) displays have always been the ideal display method because of their strong sense of reality, whereas color 3D displays of monochrome scenes are still challenging and unexplored. A color stereo reconstruction algorithm (CSRA) is proposed to solve the issue. We design a deep learning-based color stereo estimation (CSE) network to obtain color 3D information of monochrome scenes. The vivid color 3D visual effect is verified by our self-made display system. Furthermore, an efficient CSRA-based 3D image encryption scheme is achieved by encrypting a monochrome image with two-dimensional double cellular automata (2D-DCA). The proposed encryption scheme fulfills the requirement for real-time and high-security 3D image encryption with a large key space and the parallel processing capability of 2D-DCA.