DQN based single-pixel imaging.

For an orthogonal transform based single-pixel imaging (OT-SPI), to accelerate its speed while degrading as little as possible of its imaging quality, the normal way is to artificially plan the sampling path for optimizing the sampling strategy based on the characteristic of the orthogonal transform. Here, we propose an optimized sampling method using a Deep Q-learning Network (DQN), which considers the sampling process as decision-making, and the improvement of the reconstructed image as feedback, to obtain a relatively optimal sampling strategy for an OT-SPI. We verify the effectiveness of the method through simulations and experiments.

Optical encryption using uncorrelated characteristics of dynamic scattering media and spatially random sampling of a plaintext.

Scattering media are generally regarded as an obstacle in optical imaging. However, the scattering of a diffuser can be exactly taken as an advantage to act as random phase masks in the field of optical encryption to enhance information security. Here, we propose and demonstrate a dynamic diffuser based optical encryption method, which increases the ciphering strength by exploiting the uncorrelated characteristics of the dynamic diffuser as well as randomly sampling the plaintext multiple times.