Computational ghost imaging has attracted wide attention from researchers in many fields over the last two decades. Multispectral imaging as one application of computational ghost imaging possesses spatial and spectral resolving abilities, and is very useful for surveying scenes and extracting detailed information. Existing multispectral imagers mostly utilize narrow band filters or dispersive optical devices to separate light of different wavelengths, and then use multiple bucket detectors or an array detector to record them separately. Here, we propose a novel multispectral ghost imaging method that uses one single bucket detector with multiplexed illumination to produce a colored image. The multiplexed illumination patterns are produced by three binary encoded matrices (corresponding to the red, green and blue colored information, respectively) and random patterns. The results of the simulation and experiment have verified that our method can be effective in recovering the colored object. Multispectral images are produced simultaneously by one single-pixel detector, which significantly reduces the amount of data acquisition.

Long-range light detection and ranging (lidar) of active illumination optical imaging has widespread applications, such as remote sensing, satellite-based global topography, and target recognition and identification. Here, to make trade-offs among imaging efficiency, resolution, receiving field of view, divergence angle, and detected distance, we demonstrate a scanning single-pixel imaging lidar (SSPIL), enjoying the merits of the traditional pointing-by-pointing scanning imaging and single-pixel imaging. The imaging strategy of SSPIL is divided into scanning search and staring imaging processes. These strategies can save most time consumption for imaging background areas and thus improve imaging efficiency. Three imaging experiments were conducted in real urban atmospheric conditions. The preliminary results show SSPIL has the ability for long-range imaging with high efficiency, high resolution, and a large receiving field of view. Also, from the imaging results, we found that multiple samples can improve the SNR of imaging in the real urban atmosphere. The present work may provide a valuable alternative approach in the long-range active illumination optical imaging fields.

A novel technique for Radon single-pixel imaging with projective sampling, which is based on the theorem of the Radon transform, is proposed. In contrast to current patterns in conventional single-pixel imaging systems, candy-striped patterns called Radon basis patterns, which are produced by projecting the 1D Hadamard functions along different angles, are employed in our proposed technique. Here, the patterns are loaded into a projection system and then illuminated onto an object. The light reflected from the object is detected by a single-pixel detector. An iterative reconstruction method is used to restore the object’s 1D projection functions by summing the 1D Hadamard functions and detected intensities. Next, the Radon spectrum of the object is recovered by arranging the 1D projection functions along the projection angle. Finally, the image of the object can be recovered using a filtered back-projection algorithm with the Radon spectrum. Experiments demonstrate that the proposed technique can obtain the information of the Radon spectrum and image of the object. Recognition directly in the Radon spectrum domain, rather than in the image domain, is fast and yields robust and high classification rates. A recognition experiment is performed by detecting the lines in one scene by searching the singular peaks in the Radon spectrum domain. According to the results, the lines in the scene can be easily detected in the Radon spectrum domain. Other shapes can also be detected by the characteristics of those shapes in the Radon spectrum domain.

A novel technique for the simultaneous fusion, imaging and encryption of multiple objects using a single-pixel detector is proposed. Here, encoded multiplexing patterns are employed to illuminate multiple objects simultaneously. The mixed light reflected from the objects is detected by a single-pixel detector. An iterative reconstruction method is used to restore the fused image by summing the multiplexed patterns and detected intensities. Next, clear images of the objects are recovered by decoding the fused image. We experimentally obtain fused and multiple clear images by utilizing a single-pixel detector to collect the direct and indirect reflected light. Technically, by utilizing the patterns with per-pixel exposure control, multiple objects’ information is multiplexed into the detected intensities and then demultiplexed computationally under the single-pixel imaging and compressed sensing schemes. An encryption experiment is performed by setting the multiplexed patterns’ encoding as keys.