Optical single-pixel volumetric imaging by three-dimensional light-field illumination.

Three-dimensional single-pixel imaging (3D SPI) has become an attractive imaging modality for both biomedical research and optical sensing. 3D-SPI techniques generally depend on time-of-flight or stereovision principle to extract depth information from backscattered light. However, existing implementations for these two optical schemes are limited to surface mapping of 3D objects at depth resolutions, at best, at the millimeter level. Here, we report 3D light-field illumination single-pixel microscopy (3D-LFI-SPM) that enables volumetric imaging of microscopic objects with a near-diffraction-limit 3D optical resolution. Aimed at 3D space reconstruction, 3D-LFI-SPM optically samples the 3D Fourier spectrum by combining 3D structured light-field illumination with single-element intensity detection. We build a 3D-LFI-SPM prototype that provides an imaging volume of ∼390 × 390 × 3,800 µm3 and achieves 2.7-µm lateral resolution and better than 37-µm axial resolution. Its capability of 3D visualization of label-free optical absorption contrast is demonstrated by imaging single algal cells in vivo. Our approach opens broad perspectives for 3D SPI with potential applications in various fields, such as biomedical functional imaging.

Single-Pixel Imaging Based on Deep Learning Enhanced Singular Value Decomposition.

We propose and demonstrate a single-pixel imaging method based on deep learning network enhanced singular value decomposition. The theoretical framework and the experimental implementation are elaborated and compared with the conventional methods based on Hadamard patterns or deep convolutional autoencoder network. Simulation and experimental results show that the proposed approach is capable of reconstructing images with better quality especially under a low sampling ratio down to 3.12%, or with fewer measurements or shorter acquisition time if the image quality is given. We further demonstrate that it has better anti-noise performance by introducing noises in the SPI systems, and we show that it has better generalizability by applying the systems to targets outside the training dataset. We expect that the developed method will find potential applications based on single-pixel imaging beyond the visible regime.

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.

Scalable High-Resolution Single-Pixel Imaging via Pattern Reshaping.

Single-pixel imaging (SPI) is an alternative method for obtaining images using a single photodetector, which has numerous advantages over the traditional matrix-based approach. However, most experimental SPI realizations provide relatively low resolution compared to matrix-based imaging systems. Here, we show a simple yet effective experimental method to scale up the resolution of SPI. Our imaging system utilizes patterns based on Hadamard matrices, which, when reshaped to a variable aspect ratio, allow us to improve resolution along one of the axes, while sweeping of patterns improves resolution along the second axis. This work paves the way towards novel imaging systems that retain the advantages of SPI and obtain resolution comparable to matrix-based systems.

Comparison of Common Algorithms for Single-Pixel Imaging via Compressed Sensing.

Single-pixel imaging (SPI) uses a single-pixel detector instead of a detector array with a lot of pixels in traditional imaging techniques to realize two-dimensional or even multi-dimensional imaging. For SPI using compressed sensing, the target to be imaged is illuminated by a series of patterns with spatial resolution, and then the reflected or transmitted intensity is compressively sampled by the single-pixel detector to reconstruct the target image while breaking the limitation of the Nyquist sampling theorem. Recently, in the area of signal processing using compressed sensing, many measurement matrices as well as reconstruction algorithms have been proposed. It is necessary to explore the application of these methods in SPI. Therefore, this paper reviews the concept of compressive sensing SPI and summarizes the main measurement matrices and reconstruction algorithms in compressive sensing. Further, the performance of their applications in SPI through simulations and experiments is explored in detail, and then their advantages and disadvantages are summarized. Finally, the prospect of compressive sensing with SPI is discussed.

Illumination Temporal Fluctuation Suppression for Single-Pixel Imaging.

Single-pixel cameras offer improved performance in non-visible imaging compared with modern digital cameras which capture images with an array of detector pixels. However, the quality of the images reconstructed by single-pixel imaging technology fails to match traditional cameras. Since it requires a sequence of measurements to retrieve a single image, the temporal fluctuation of illumination intensity during the measuring will cause inconsistence for consecutive measurements and thus noise in reconstructed images. In this paper, a normalization protocol utilizing the differential measurements in single-pixel imaging is proposed to reduce such inconsistence with no additional hardware required. Numerical and practical experiments are performed to investigate the influences of temporal fluctuation of different degrees on image quality and to demonstrate the feasibility of the proposed normalization protocol. Experimental results show that our normalization protocol can match the performance of the system with the reference arm. The proposed normalization protocol is straightforward with the potential to be easily applied in any temporal-sequence imaging strategy.

Single Pixel Imaging Key for Holographic Encryption Based on Spatial Multiplexing Metasurface.

Compared with the traditional holographic technology, metasurface holography is a promising technology due to the large field angle and high spatial resolution. Thanks to the precise control of phase, amplitude, polarization and so on, metasurface holography provides a flexible platform for light modulation, optical encryption and so on. Besides, the process of image reconstruction by single pixel imaging is similar to a form of encoding and decoding, which is realized by calculating the correlation between a series of modulation patterns and their corresponding intensity signals. In this work, an optical encryption scheme is proposed based on spatial multiplexing metasurface, which depends on the combination of holographic technology and single pixel imaging technology. In the encryption scheme, the image transmitted by single pixel imaging based on metasurface is used as the addressing key of holography. Besides, illuminating different positions of the metasurface can generate different holographic reconstructed images, and there is 50% information overlapped between adjacent sub-holograms. This work makes use of the spatial multiplexing property of metasurface, which can complete different functions, paving the way for the application in the field of optical imaging encryption and information security.

Multi-Object Positioning and Imaging Based on Single-Pixel Imaging Using Binary Patterns.

Single-pixel imaging (SPI) is a new type of imaging technology that uses a non-scanning single-pixel detector to image objects and has important application prospects and value in many fields. Most of the modulators currently used in SPI systems are digital micromirror device (DMD) modulators, which use a higher frequency for binary modulation than other alternatives. When modulating grayscale information, the modulation frequency is significantly reduced. This paper conducts research on multiple discrete objects in a scene and proposes using binary patterns to locate and image these objects. Compared with the existing methods of using gray patterns to locate and image multiple objects, the method proposed in this paper is more suitable for DMD-type SPI systems and has wider applicability and greater prospects. The principle of the proposed method is introduced, and the effectiveness of the method is experimentally verified. The experimental results show that, compared to traditional SPI methods, the number of patterns required by the proposed method is reduced by more than 85%.

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.

Experimental Study of Ghost Imaging in Underwater Environment.

Underwater imaging technique is a crucial tool for humans to develop, utilize, and protect the ocean. We comprehensively compare the imaging performance of twenty-four ghost imaging (GI) methods in the underwater environment. The GI methods are divided into two types according to the illumination patterns, the random and orthogonal patterns. Three-group simulations were designed to show the imaging performance of the twenty-four GI methods. Moreover, an experimental system was built, and three-group experiments were implemented. The numerical and experimental results demonstrate that the orthogonal pattern-based compressed sensing GI methods have strong antinoise capability and can restore clear images for underwater objects with a low measurement number. The investigation results are helpful for the practical applications of the underwater GI.