Video snapshot compressive imaging using adaptive progressive coding for high-quality reconstruction under different illumination circumstances.

We consider capturing high-speed color video under different illumination conditions using a video snapshot compressive imaging system (video SCI). An adaptive progressive coding method is proposed, and we conduct an integrated design of the imaging system in terms of optics, mechanics, and control. Compared to previous video SCI systems, this adaptive progressive coding method mitigates the image stability issues in various illumination conditions, ensuring high-quality imaging while greatly improving the light throughput of the system. Based on the analysis of both simulation and real experimental results, we found that this imaging system can achieve color video shooting under an illumination range of 2 lux to 60 lux.

Color computational ghost imaging based on a plug-and-play generalized alternating projection.

Computational ghost imaging (CGI), in which an image is retrieved from the known speckle patterns that illuminate the object and the total transmitted intensity, has shown great advances because of its advantages and potential applications at all wavelengths. However, high-quality and less time-consuming imaging has been proven challenging especially in color CGI. In this paper, we will present a new color CGI method that can achieve the reconstruction of high-fidelity images at a relatively low sampling rate (0.0625) by using plug-and-play generalized alternating projection algorithm (PnP-GAP). The spatial distribution and color information of the object are encoded into a one-dimensional light intensity sequence simultaneously by combining randomly distributed speckle patterns and a Bayer color mask as modulation patterns, which is measured by a single-pixel detector. A pre-trained deep denoising network is utilized in the PnP-GAP algorithm to achieve better results. Furthermore, a joint reconstruction and demosaicking method is developed to restore the target color information more realistically. Simulations and optical experiments are performed to verify the feasibility and superiority of our proposed scheme by comparing it with other classical reconstruction algorithms. This new color CGI scheme will enable CGI to obtain information in real scenes more effectively and further promote its practical applications.

Non-line-of-sight imaging based on an untrained deep decoder network.

In recent years, low-cost high-quality non-line-of-sight (NLOS) imaging by a passive light source has been a significant research dimension. Here, we report a new, to the best of our knowledge, reconstruction method for the well-known "occluder-aided" NLOS imaging configuration based on an untrained deep decoder network. Using the interaction between the neural network and the physical forward model, the network weights can be automatically updated without the need for training data. Completion of the optimization process facilitates high-quality reconstructions of hidden scenes from photographs of a blank wall under high ambient light conditions. Simulations and experiments show the superior performance of the proposed method in terms of the details and the robustness of the reconstructed images. Our method will further promote the practical application of NLOS imaging in real scenes.

Cryptographic analysis on an optical random-phase-encoding cryptosystem for complex targets based on physics-informed learning.

Optical cryptanalysis based on deep learning (DL) has grabbed more and more attention. However, most DL methods are purely data-driven methods, lacking relevant physical priors, resulting in generalization capabilities restrained and limiting practical applications. In this paper, we demonstrate that the double-random phase encoding (DRPE)-based optical cryptosystems are susceptible to preprocessing ciphertext-only attack (pCOA) based on DL strategies, which can achieve high prediction fidelity for complex targets by using only one random phase mask (RPM) for training. After preprocessing the ciphertext information to procure substantial intrinsic information, the physical knowledge DL method based on physical priors is exploited to further learn the statistical invariants in different ciphertexts. As a result, the generalization ability has been significantly improved by increasing the number of training RPMs. This method also breaks the image size limitation of the traditional COA method. Optical experiments demonstrate the feasibility and the effectiveness of the proposed learning-based pCOA method.

A Multi-Image Encryption Based on Sinusoidal Coding Frequency Multiplexing and Deep Learning.

Multi-image encryption technology is a vital branch of optical encryption technology. The traditional encryption method can only encrypt a small number of images, which greatly restricts its application in practice. In this paper, a new multi-image encryption method based on sinusoidal stripe coding frequency multiplexing and deep learning is proposed to realize the encryption of a greater number of images. In the process of encryption, several images are grouped, and each image in each group is first encoded with a random matrix and then modulated with a specific sinusoidal stripe; therefore, the dominant frequency of each group of images can be separated in the Fourier frequency domain. Each group is superimposed and scrambled to generate the final ciphertext. In the process of decryption, deep learning is used to improve the quality of decrypted image and the decryption speed. Specifically, the obtained ciphertext can be sent into the trained neural network and then the plaintext image can be reconstructed directly. Experimental analysis shows that when 32 images are encrypted, the CC of the decrypted result can reach more than 0.99. The efficiency of the proposed encryption method is proved in terms of histogram analysis, adjacent pixels correlation analysis, anti-noise attack analysis and resistance to occlusion attacks analysis. The encryption method has the advantages of large amount of information, good robustness and fast decryption speed.