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.

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.