Compressive measurements benefit low-light-level imaging (L³-imaging) due to the significantly improved measurement signal-to-noise ratio (SNR). However, as with other compressive imaging (CI) systems, compressive L³-imaging is slow. To accelerate the data acquisition, we develop an algorithm to compute the optimal binary sensing matrix that can minimize the image reconstruction error. First, we make use of the measurement SNR and the reconstruction mean square error (MSE) to define the optimal gray-value sensing matrix. Then, we construct an equality-constrained optimization problem to solve for a binary sensing matrix. From several experimental results, we show that the latter delivers a similar reconstruction performance as the former, while having a smaller dynamic range requirement to system sensors.

In the mid-wave infrared (MIR) band, large detector arrays are extremely costly and technically difficult to be manufactured. Thus, it is difficult to obtain high-resolution images for a conventional MIR camera. Spatial compressive imaging can improve resolution. However, system errors due to misalignment or optical aberrations degrade reconstruction quality significantly. Another common issue for compressive imaging is the slow imaging speed, which is caused by slow measurement collection and reconstruction processes. To deal with the two issues, we use an imaging calibration method to improve reconstruction quality and a sliding window measurement collection strategy plus a reconstruction algorithm accelerated by parallel computing to fasten the speed. We build a prototype of a compressive imaging camera with an angular resolution 1.17 lp/mrad. A four-bar target is used as an object. We reconstruct a moving scene of size 1280 × 1024 with a frame rate 20 frames per second.

Without decreasing spatial resolution, temporal compressive imaging (TCI) can improve the temporal resolution of an imaging sensor and relax the requirement of the data readout speed in high-speed imaging. In this Letter, we describe a near-infrared TCI system that can reconstruct 500 fps videos from coded measurement frames sampled at 50 fps.

Free-space Brillouin lasers (BLs) generate high-power narrow-linewidth laser outputs at specific wavelengths. However, despite these impressive results, further compressing the Stokes linewidth while maintaining a high-power output remains a challenge. This study was conducted to investigate the design of BLs, starting from the phase-matching conditions and stability of a Brillouin cavity, for media with different phonon frequencies. A high-power and narrow-linewidth output can be realized using large-size, wide-gain-bandwidth Brillouin media. We experimentally demonstrated a BL using fused silica and diamond with the same cavity parameters. Stokes powers of 21.6 and 18.9 W were obtained with fused silica and diamond, respectively, at an available pump power of 58 W. Using fused silica, a Stokes linewidth of 1.2 kHz was obtained, which is two times narrower than that obtained using the diamond BL. Such BL design routes for various Brillouin media provide a path for achieving high-power, ultra-narrow-linewidth laser radiation at specific wavelengths.

Different from conventional decomposition methods, which utilize several steps to obtain the final result, a self-attention-based neural network, Attention Full-waveform Decomposition Network (AFD-Net), is discussed in this article for end-to-end full-waveform LiDAR signal decomposition. In existing LiDAR waveform decomposition methods, complicated functional models are used to fit echo components. Thus, the echo decomposition problem can be translated into a function approximation task. Recent studies present great progress in estimating the parameters of fitting models, hence, in the final decomposition results. However, the shape of received echo components is always irregular. None of the parametric functional models can fit the received echo components perfectly, which leads to unavoidable errors in the initial step of echo decomposition. In this article, we propose an end-to-end network AFD-Net to solve the echo decomposition problem without assuming any parametric functional models. AFD-Net consists of two modules: 1) the classification module and 2) the decomposition module. The former module is used to determine the number of echo components in a received waveform. Then, the decomposition module is used to output the echo components. By experiments, we have a classification accuracy of 96 $\%$ using the first module. The average $R^{2}$ coefficient for the decomposed echo components is 0.9799. In addition, there are no public datasets for the waveform decomposition task available. Thus, another contribution of our work is to develop a tool to generate synthetic full-waveform LiDAR signals, which can help researchers to construct their own dataset for related works.