Optical Encryption Using Attention-Inserted Physics-Driven Single-Pixel Imaging.

Optical encryption based on single-pixel imaging (SPI) has made great advances with the introduction of deep learning. However, the use of deep neural networks usually requires a long training time, and the networks need to be retrained once the target scene changes. With this in mind, we propose an SPI encryption scheme based on an attention-inserted physics-driven neural network. Here, an attention module is used to encrypt the single-pixel measurement value sequences of two images, together with a sequence of cryptographic keys, into a one-dimensional ciphertext signal to complete image encryption. Then, the encrypted signal is fed into a physics-driven neural network for high-fidelity decoding (i.e., decryption). This scheme eliminates the need for pre-training the network and gives more freedom to spatial modulation. Both simulation and experimental results have demonstrated the feasibility and eavesdropping resistance of this scheme. Thus, it will lead SPI-based optical encryption closer to intelligent deep encryption.

Multi-Party Cryptographic Key Distribution Protocol over a Public Network Based on a Quick-Response Code.

In existing cryptographic key distribution (CKD) protocols based on computational ghost imaging (CGI), the interaction among multiple legitimate users is generally neglected, and the channel noise has a serious impact on the performance. To overcome these shortcomings, we propose a multi-party interactive CKD protocol over a public network, which takes advantage of the cascade ablation of fragment patterns (FPs). The server splits a quick-response (QR) code image into multiple FPs and embeds different "watermark" labels into these FPs. By using a CGI setup, the server will acquire a series of bucket value sequences with respect to different FPs and send them to multiple legitimate users through a public network. The users reconstruct the FPs and determine whether there is an attack in the public channel according to the content of the recovered "watermark" labels, so as to complete the self-authentication. Finally, these users can extract their cryptographic keys by scanning the QR code (the cascade ablation result of FPs) returned by an intermediary. Both simulation and experimental results have verified the feasibility of this protocol. The impacts of different attacks and the noise robustness have also been investigated.

Secondary Complementary Balancing Compressive Imaging with a Free-Space Balanced Amplified Photodetector.

Single-pixel imaging (SPI) has attracted widespread attention because it generally uses a non-pixelated photodetector and a digital micromirror device (DMD) to acquire the object image. Since the modulated patterns seen from two reflection directions of the DMD are naturally complementary, one can apply complementary balanced measurements to greatly improve the measurement signal-to-noise ratio and reconstruction quality. However, the balance between two reflection arms significantly determines the quality of differential measurements. In this work, we propose and demonstrate a simple secondary complementary balancing mechanism to minimize the impact of the imbalance on the imaging system. In our SPI setup, we used a silicon free-space balanced amplified photodetector with 5 mm active diameter which could directly output the difference between two optical input signals in two reflection arms. Both simulation and experimental results have demonstrated that the use of secondary complementary balancing can result in a better cancellation of direct current components of measurements, and can acquire an image quality slightly better than that of single-arm single-pixel complementary measurement scheme (which is free from the trouble of optical imbalance) and over 20 times better than that of double-arm dual-pixel complementary measurement scheme under optical imbalance conditions.

Gradient-Descent-like Ghost Imaging.

Ghost imaging is an indirect optical imaging technique, which retrieves object information by calculating the intensity correlation between reference and bucket signals. However, in existing correlation functions, a high number of measurements is required to acquire a satisfied performance, and the increase in measurement number only leads to limited improvement in image quality. Here, inspired by the gradient descent idea that is widely used in artificial intelligence, we propose a gradient-descent-like ghost imaging method to recursively move towards the optimal solution of the preset objective function, which can efficiently reconstruct high-quality images. The feasibility of this technique has been demonstrated in both numerical simulation and optical experiments, where the image quality is greatly improved within finite steps. Since the correlation function in the iterative formula is replaceable, this technique offers more possibilities for image reconstruction of ghost imaging.

Pseudo-thermal imaging by using sequential-deviations for real-time image reconstruction.

Ghost imaging technologies acquire images through intensity correlation of reference patterns and bucket values. Among them, an interesting method named correspondence imaging can generate positive-negative images by only conditionally averaging reference patterns, but still requires full/over sampling to treat the ensemble average of bucket values as a selection criteria, causing a long acquisition time. Here, we propose a sequential-deviation ghost imaging approach, which can realize real-time reconstructions of positive-negative images with a high image quality close to that of differential ghost imaging. Since it is no longer necessary to compare with the ensemble average, this method can improve the real-time performance. An explanation of its essence is also given here. Both simulation and experimental results have demonstrated the feasibility of this technique. This work may complement the theory of ghost imaging.