Quantum dot-enabled infrared hyperspectral imaging with single-pixel detection.

Near-infrared (NIR) hyperspectral imaging is a powerful technique that enables the capture of three-dimensional (3D) spectra-spatial information within the NIR spectral range, offering a wide array of applications. However, the high cost associated with InGaAs focal plane array (FPA) has impeded the widespread adoption of NIR hyperspectral imaging. Addressing this challenge, in this study, we adopt an alternative approach-single-pixel detection for NIR hyperspectral imaging. Our investigation reveals that single-pixel detection outperforms conventional FPA, delivering a superior signal-to-noise ratio (SNR) for both spectral and imaging reconstruction. To implement this strategy, we leverage self-assembled colloidal quantum dots (CQDs) and a digital micromirror device (DMD) for NIR spectral and spatial information multiplexing, complemented by single-pixel detection for simultaneous spectral and image reconstruction. Our experimental results demonstrate successful NIR hyperspectral imaging with a detection window about 600 nm and an average spectral resolution of 8.6 nm with a pixel resolution of 128 × 128. The resulting spectral and spatial data align well with reference instruments, which validates the effectiveness of our approach. By circumventing the need for expensive and bulky FPA and wavelength selection components, our solution shows promise in advancing affordable and accessible NIR hyperspectral imaging technologies, thereby expanding the range of potential applications.

High-speed generation of non-Rayleigh speckle.

Speckle with non-Rayleigh amplitude distribution has significant research value in imaging and measurement using structured illumination. However, existing speckle customizing schemes have been limited in generation speed due to the refresh rate of spatial light modulators (SLMs). In this work, we proposed a method to rapidly generate non-Rayleigh distributed speckle fields using a digital micro-mirror device (DMD). In contrast to SLMs that allow for gray-scale phase modulation, DMD is limited to binary amplitude control. To solve this limitation, we design a Gerchberg-Saxton-like algorithm based on super-pixel method, this algorithm enables the customization of non-Rayleigh speckle with arbitrary intensity probability density function. Statistical analyses of experimental results have demonstrated that the customized speckles exhibit excellent stability in their lateral statistical properties, while also maintaining consistent propagation characteristics with Rayleigh speckle in the longitudinal direction. This method provides a new approach for high-speed and arbitrary intensity speckle customization, holding potential applications in imaging, measurement, and encryption fields.

Rationally Structured Triboelectric Nanogenerator Arrays for Harvesting Water-Current Energy and Self-Powered Sensing.

Water-current energy is an enormous and widely distributed clean energy in nature, with different scales from large ocean flow to small local turbulence. However, few effective technologies have been proposed to make use of different forms of water currents as a power source. Here, high-performance paired triboelectric nanogenerators (P-TENGs) capable of integrating massively into a thin flexible layer as a structured triboelectric surface (STS) are demonstrated for harvesting water-current energy. Novel gas packet exchange structure and rigid-flexible coupling deformation mechanism are introduced to ensure that the device can work very effectively even in deep water under high water pressure. The rationally designed TENG array in the STS enables highly efficient power take-off from the flow. Typically, the STS demonstrates a high-frequency output up to 57 Hz, largely superior to current TENG devices, and the power density is improved by over 100 times for triboelectric devices harvesting current energy. The flexible STS is capable of attaching to various surfaces or applying independently for self-powered sensing and underwater power supply, showing great potential for water-current energy utilization. Moreover, the work also initiates universal strategies to fabricate high-frequency devices under large environment pressure, which may profoundly enrich the design of TENGs.

Dictionary-based compressive Fourier ptychography.

Fourier ptychography (FP) provides an alternative scheme for improving the spatial bandwidth product with limited device. However, an angle-variation illumination is necessary to realize scanning imaging in the Fourier plane, which dramatically restricts the imaging speed and detection efficiency. In this paper, we propose a multiplexing and compressible FP scheme based on the structured illuminations and compressive sensing technique. Half of the LEDs are lighted together to reduce the exposure time; meanwhile, a learned dictionary is employed to reduce the sampling times. In addition, spectral independent illumination is proposed to retrieve color information from monochrome samplings. We experimentally verify that the proposed method can effectively reduce the sampling time with limited resolution loss.

Noninvasive imaging of two isolated objects through a thin scattering medium beyond the 3D optical memory effect by speckle-based difference strategy.

The shape of two objects hidden behind a thin scattering medium is retrieved by the presented method. One of the two objects keeps stationary, while the other one is supposed to be gradually moving, and the Euclidean distance between them is always beyond the range of the 3D optical memory effect. We capture two speckle patterns to image the two isolated objects by using a developed speckle-differential-based strategy and the traditional speckle autocorrelation technique. The feasibility of our method is demonstrated by theoretical analysis and a set of experiments.

New strategy for high-dimensional single-pixel imaging.

Single-pixel imaging (SPI) technique has been studied intensively due to its minimum requirement for the detector resolution and the equipment costs. In this work, we proposed a new strategy of the SPI to explore its capability in high-dimensional imaging, which is the first comprehensive scheme as we know to achieve calibration, color texture and viewpoint expansion of single-pixel three-dimensional imaging. We realized a low-cost single-pixel three-dimensional imaging scheme which employ a raster scanner to provide the structured illumination and a grating to encode the height information. In order to reduce the blocking area, we introduce two single-pixel detectors (SPDs) to detect from two detection angles, a modified total variation based criterion is proposed to fuse the height information from two SPDs and reduce the error of shape fusion. To acquire the information of higher dimension, we introduce the third SPD aims to gain the color texture, three bandpass filter is placed in front of three SPDs, respectively, to collect different color information. Meanwhile a viewpoint switching method inspired by the shape from shading theory is presented to improve the color fidelity. Our study is expected to provide a demonstration for SPI in acquisition, reconstruction, and fusion of high-dimensional image data.

Noise Suppression in Compressive Single-Pixel Imaging.

Compressive single-pixel imaging (CSPI) is a novel imaging scheme that retrieves images with nonpixelated detection. It has been studied intensively for its minimum requirement of detector resolution and capacity to reconstruct image with underdetermined acquisition. In practice, CSPI is inevitably involved with noise. It is thus essential to understand how noise affects its imaging process, and more importantly, to develop effective strategies for noise compression. In this work, two ypes of noise classified as multiplicative and additive noises are discussed. A normalized compressive reconstruction scheme is firstly proposed to counteract multiplicative noise. For additive noise, two types of compressive algorithms are studied. We find that pseudo-inverse operation could render worse reconstructions with more samplings in compressive sensing. This problem is then solved by introducing zero-mean inverse measurement matrix. Both experiment and simulation results show that our proposed algorithms significantly surpass traditional methods. Our study is believed to be helpful in not only CSPI but also other denoising works when compressive sensing is applied.

Imaging high-speed moving targets with a single-pixel detector.

Single-pixel imaging (SPI) has recently been intensively studied as an alternative to the traditional focal plane array (FPA) technology. However, limited by the refresh rate of spatial light modulators (SLM) and inherent reconstruction mechanism, SPI is inappropriate for high-speed moving targets. To break through this limitation, we propose a novel SPI scheme for high-speed moving targets. In our scenario, the spatial encoding for the target is done by the movement of the target relative to a static pseudo-random illumination pattern. In this process, a series of single-pixel signals are generated that corresponds to the overlap between the target and certain parts of the illumination structure. This correspondence can be utilized for image reconstruction in the same way as normal SPI. In addition, compressive sensing and deep learning algorithms are used for reconstruction, respectively. Reasonable reconstructions can be obtained with a sampling ratio of only 6%. Experimental verification together with theoretical analysis has shown that our scheme is able to image high-speed moving targets that could be alternatively achieved by a fast FPA camera. Our scheme keeps the inherent advantages of SPI and meanwhile extend its application to moving targets. It is believed that this technology will have wide application in many situations.

Scan efficiency of structured illumination in iterative single pixel imaging.

Single-pixel imaging (SPI) is an innovative technique that images an object from non-pixelated detection. To do so, SPI has to conduct structured illumination that functions as a way to scan the object. The illumination basis and corresponding scanned intensities are then used for correlation measurement to reconstruct an image. In this process, the illumination structure, or scanning basis plays an important role on the scanning efficiency and therefore reconstruction quality. In this work we discuss the efficiency of different scanning basis in iterative SPI. A comparison between raster scan (RS) and multi-pixel structured scan (MS) in SPI is carried out under the criterions of signal to noise ratio and structural similarity index. Theoretical analysis is followed with demonstration from both experiment and simulation. Our conclusion is believed to be useful guidelines for choosing the right illumination basis depending on the iterative SPI application situation.

Singular value decomposition ghost imaging.

The singular value decomposition ghost imaging (SVDGI) is proposed to enhance the fidelity of computational ghost imaging (GI) by constructing a measurement matrix using singular value decomposition (SVD) transform. After SVD transform on a random matrix, the non-zero elements of singular value matrix are all made equal to 1.0, then the measurement matrix is acquired by inverse SVD transform. Eventually, the original objects can be reconstructed by multiplying the transposition of the matrix by a series of collected intensity. SVDGI enables the reconstruction of an N-pixel image using much less than N measurements, and perfectly reconstructs original object with N measurements. Both the simulated and the optical experimental results show that SVDGI always costs less time to accomplish better works. Firstly, it is at least ten times faster than GI and differential ghost imaging (DGI), and several orders of magnitude faster than pseudo-inverse ghost imaging (PGI). Secondly, in comparison with GI, the clarity of SVDGI can get sharply improved, and it is more robust than the other three methods so that it yields a clearer image in the noisy environment.