In the conventional weighted Gerchberg-Saxton (GS) algorithm, the feedback is used to accelerate the convergence. However, it will lead to the iteration divergence. To solve this issue, an adaptive weighted GS algorithm is proposed in this paper. By replacing the conventional feedback with our designed feedback, the convergence can be ensured in the proposed method. Compared with the traditional GS iteration method, the proposed method improves the peak signal-noise ratio of the reconstructed image with 4.8 dB on average. Moreover, an approximate quadratic phase is proposed to suppress the artifacts in optical reconstruction. Therefore, a high-quality image can be reconstructed without the artifacts in our designed Argument Reality device. Both numerical simulations and optical experiments have validated the effectiveness of the proposed method.

In this paper, a color image encryption is proposed based on the dynamic DNA and the 4-D memristive hyper-chaos. Firstly, chaotic matrices are generated from the 4-D memristive hyper-chaos by using the plain image, the salt key and control parameters. Secondly, the dynamic encoding process is performed on three components of the plain image to obtain three DNA matrices, respectively. Thirdly, to enhance both the security and robustness of encrypted image, dynamic confusion and diffusion are applied to the encoded DNA matrices, respectively. Finally, the encrypted image is generated by DNA decoding and components combining. The main feature of our proposed algorithm is that dynamic DNA mechanism based on hyper-chaos is performed on the processes of encoding, confusion and diffusion. Simulation results and security analysis further demonstrate that it has a strong resistance against various attacks and outperforms other methods in the literature.

A 3D display based on a parallax barrier with multiview zones is proposed. This display consists of a 2D display panel and a parallax barrier. The basic element of the parallax barrier has three narrow slits. They can show three columns of subpixels on the 2D display panel and form 3D pixels. The parallax barrier can provide multiview zones. In these multiview zones, the proposed 3D display can use a small number of views to achieve a high density of views. Therefore, the distance between views is the same as the conventional ones with more views. Considering the proposed display has fewer views, which bring more 3D pixels in the 3D images, the resolution and brightness will be higher than the conventional ones. A 12-view prototype of the proposed 3D display is developed, and it provides the same density of views as a conventional one with 28 views. Experimental results show the proposed display has higher resolution and brightness than the conventional one. The cross talk is also limited at a low level.

In this paper, a multi-image encryption scheme based on compressed sensing (CS) and deep learning in the optical gyrator domain is proposed. Firstly, multiple plaintext images are compressed by CS to obtain multiple measurements, and then the pixels of each measurement are scrambled by using a chaotic system. Secondly, the scrambled measurements are combined into a matrix and diffused by XOR operation with a chaotic matrix. Finally, the diffused matrix is encoded with a random phase and an optical gyrator transform to obtain a complex-valued matrix, and the amplitude of the complex-valued matrix is taken as the ciphertext. In decrypt, plaintext images are reconstructed from the CS measurements by a neural network, which achieves high reconstruction speed and quality compared with the traditional algorithm. Especially, the data amount of plaintext images can be compressed by up to 8 times while achieving high decryption quality. To our best knowledge, CS reconstruction algorithms based on deep learning is firstly used for image encryption. Moreover, the proposed scheme is highly robust against occlusion, noise, and chosen-plaintext attack.