LAUSR

Computational ghost imaging (CGI) can reconstruct the pixelated image of a target without lenses and image sensors. In almost all spatial CGI systems using various patterns reported in the past, people often only focus on the distribution of patterns in the spatial dimension but ignore the possibility of encoding in the time dimension or even the space-time dimension. Although the random illumination pattern in CGI always brings some inevitable background noise to the recovered image, it has considerable advantages in optical encryption, authentication, and watermarking technologies. In this paper, we focus on stimulating the potential of random lighting patterns in the space-time dimension for embedding large amounts of information. Inspired by binary CGI and second-order correlation operations, we design two novel generation schemes of pseudo-random patterns for information embedding that are suitable for different scenarios. Specifically, we embed a total of 10,000 ghost images (64 × 64 pixels) of the designed Hadamard-matrix-based data container patterns in the framework of CGI, and these ghost images can be quantitatively decoded to two 8-bit standard grayscale images, with a total data volume of 1, 280, 000 bits. Our scheme has good noise resistance and a low symbol error rate. One can design the number of lighting patterns and the information capacity of the design patterns according to the trade-off between accuracy and efficiency. Our scheme, therefore, paves the way for CGI using random lighting patterns to embed large amounts of information and provides new insights into CGI-based encryption, authentication, and watermarking technologies.