Imaging through non-static and optically thick scattering media such as dense fog, heavy smoke, and turbid water is crucial in various applications. However, most existing methods rely on either active and coherent light illumination, or image priors, preventing their application in situations where only passive illumination is possible. In this study we present a universal passive method for imaging through dense scattering media that does not depend on any prior information. Combining the selection of small-angle components out of the incoming information-carrying scattering light and image enhancement algorithm that incorporates time-domain minimum filtering and denoising, we show that the proposed method can dramatically improve the signal-to-interference ratio and contrast of the raw camera image in outfield experiments.

High resolution imaging is achieved using increasingly larger apertures and successively shorter wavelengths. Optical aperture synthesis is an important high-resolution imaging technology used in astronomy. Conventional long baseline amplitude interferometry is susceptible to uncontrollable phase fluctuations, and the technical difficulty increases rapidly as the wavelength decreases. The intensity interferometry inspired by HBT experiment is essentially insensitive to phase fluctuations, but suffers from a narrow spectral bandwidth which results in a lack of effective photons. In this study, we propose optical synthetic aperture imaging based on spatial intensity interferometry. This not only realizes diffraction-limited optical aperture synthesis in a single shot, but also enables imaging with a wide spectral bandwidth, which greatly improves the optical energy efficiency of intensity interferometry. And this method is insensitive to the optical path difference between the sub-apertures. Simulations and experiments present optical aperture synthesis diffraction-limited imaging through spatial intensity interferometry in a 100 nm spectral width of visible light, whose maximum optical path difference between the sub-apertures reaches 69λ. This technique is expected to provide a solution for optical aperture synthesis over kilometer-long baselines at optical wavelengths.

To increase the storage capacity in holographic data storage (HDS), the information to be stored is encoded into a complex amplitude. Fast and accurate retrieval of amplitude and phase from the reconstructed beam is necessary during data readout in HDS. In this study, we proposed a complex amplitude demodulation method based on deep learning from a single-shot diffraction intensity image and verified it by a non-interferometric lensless experiment demodulating four-level amplitude and four-level phase. By analyzing the correlation between the diffraction intensity features and the amplitude and phase encoding data pages, the inverse problem was decomposed into two backward operators denoted by two convolutional neural networks (CNNs) to demodulate amplitude and phase respectively. The experimental system is simple, stable, and robust, and it only needs a single diffraction image to realize the direct demodulation of both amplitude and phase. To our investigation, this is the first time in HDS that multilevel complex amplitude demodulation is achieved experimentally from one diffraction intensity image without iterations.

A planar-integrated optical system (PIOS) represents powerful optical imaging and information processing techniques and is a potential candidate for the realization of a three-dimensional (3D) integrated optoelectronic intelligent system. Coupling the optical wave carrying information into a planar transparent substrate (typically fused silica) is an essential prerequisite for the realization of such a PIOS. Unlike conventional grating couplers for nano-waveguides on the silicon-on-insulator platform, the grating couplers for PIOS enable to obtain a higher design freedom and to achieve much higher coupling efficiency. By combining the rigorous coupled wave algorithm and simulated annealing optimization algorithm, a high-efficiency asymmetric double-groove grating coupler is designed for PIOS. It is indicated that, under the condition of the normal incidence of TE polarization, the diffraction efficiency of the -1st order is over 95%, and its average value is 97.3% and 92.8% in the C and C+L bands. The simulation results indicate that this type of grating coupler has good tolerance and is expected to be applied in optical interconnections, waveguide-based augmented reality glasses, and planar-integrated 3D interconnection optical computing systems.

Single-pixel imaging (SPI) is a typical computational imaging modality that allows two- and three-dimensional image reconstruction from a one-dimensional bucket signal acquired under structured illumination. It is in particular of interest for imaging under low light conditions and in spectral regions where good cameras are unavailable. However, the resolution of the reconstructed image in SPI is strongly dependent on the number of measurements in the temporal domain. Data-driven deep learning has been proposed for high-quality image reconstruction from a undersampled bucket signal. But the generalization issue prohibits its practical application. Here we propose a physics-enhanced deep learning approach for SPI. By blending a physics-informed layer and a model-driven fine-tuning process, we show that the proposed approach is generalizable for image reconstruction. We implement the proposed method in an in-house SPI system and an outdoor single-pixel LiDAR system, and demonstrate that it outperforms some other widespread SPI algorithms in terms of both robustness and fidelity. The proposed method establishes a bridge between data-driven and model-driven algorithms, allowing one to impose both data and physics priors for inverse problem solvers in computational imaging, ranging from remote sensing to microscopy.

Optical cryptanalysis is essential to the further investigation of more secure optical cryptosystems. Learning-based attack of optical encryption eliminates the need for the retrieval of random phase keys of optical encryption systems but it is limited for practical applications since it requires a large set of plaintext-ciphertext pairs for the cryptosystem to be attacked. Here, we propose a two-step deep learning strategy for ciphertext-only attack (COA) on the classical double random phase encryption (DRPE). Specifically, we construct a virtual DRPE system to gather the training data. Besides, we divide the inverse problem in COA into two more specific inverse problems and employ two deep neural networks (DNNs) to respectively learn the removal of speckle noise in the autocorrelation domain and the de-correlation operation to retrieve the plaintext image. With these two trained DNNs at hand, we show that the plaintext can be predicted in real-time from an unknown ciphertext alone. The proposed learning-based COA method dispenses with not only the retrieval of random phase keys but also the invasive data acquisition of plaintext-ciphertext pairs in the DPRE system. Numerical simulations and optical experiments demonstrate the feasibility and effectiveness of the proposed learning-based COA method.