In practical application environments, objects are rarely stationary, which makes it difficult to image dynamic objects with conventional single-pixel imaging (SI) techniques. In this paper, a rotationally synchronized single-pixel imaging (RS-SI) method is proposed to image a dynamic object in rotation. The modulation pattern rotates in sync with the rapidly rotating object, and the center of object rotation is ensured to be in line with the center of the illumination pattern. Then, RS-SI reconstruction algorithm is used to reconstruct the image of the rotating object by collecting the light reflected from the object's surface. This method does not require advanced knowledge of the object rotation speed to complete the imaging. Simulation and experimental results confirm that the RS-SI can reconstruct images of an object at rotational speeds up to 422.0 rpm and can also complete imaging of a variable-speed rotating object. Imaging results of 128 × 128 pixels at a sampling rate of 27.47% can be obtained with high fidelity. The proposed RS-SI is a method for imaging an object in rotation, providing insights for future applications of single-pixel imaging technology for defect detection in rotating parts.

Single-pixel imaging (SPI), which uses a photodetector to detect the reflected total light intensity of a set of structured illumination patterns modulated by a target scene, provides a method for visible waveband imaging, hyperspectral imaging, and terahertz imaging. However, it faces a challenge when the scene to be imaged has specular reflections. To deal with this problem, a multi-angle method without feature matching is presented. With this method, the location of the detector does not affect image reconstruction, and the results of reconstruction at each location are matched at the pixel level automatically. In simulations, with the original image as a reference, the structural similarity index value of the picture obtained by the proposed method is 10% higher than the picture obtained from a single angle. The signal-to-noise ratio value of the picture obtained by the proposed method is 4.424, which is higher than 1.577 of the maximum value of the reconstruction result from a single angle. To evaluate the method, a metal key and an aircraft engine blade with specular reflections are taken as the target scene and are reconstructed from four different imaging perspectives, giving results that are matched at the pixel level. The final reconstructed image is obtained using the principal component analysis algorithm or the fourth-order partial differential equations and principal component analysis algorithm. Compared with the image obtained from a single angle, the correlation coefficient between the image obtained by the proposed method and the reference image is increased from the minimum value of 0.3139 to 0.7050, and the power ratio is increased from 4.52% to 73.63%. The proposed method has great potential specifically for improving the quality of SPI for scenes exhibiting specular reflections.

Single-pixel imaging technology is popular with invisible wavelengths and low light environments. However, the time-consuming steps hindered the development of single-pixel imaging technology. To improve imaging efficiency, a high-efficiency one-step single-pixel imaging method based on the discrete Hartley transform is proposed. The proposed method does not require a large number of fringe patterns and only requires a real-number calculation. The number of fringe patterns required for the proposed method is only half of that required for the four-step phase-shift Fourier method at the same sampling rate. Although a one-step method, it also uses the idea of differential measurements and adds upsampling processing strategies, which simultaneously improve the signal-to-noise ratio of the recovered image. The simulation shows that the peak signal-to-noise ratio and structural similarity index of the recovered target scene exceed 20 dB and 80%, respectively, when the sampling rate is 30%. Only 20 164 patterns are needed to reconstruct a (256 × 256)-pixel image. After defocusing the gray stripe pattern into a binary pattern, it only takes milliseconds to project these patterns into the target. It can be seen that the experimental results of the proposed method are significantly better than those of the two-step phase-shift method under dramatical noise interference. With the rapid development of advanced equipment, this method will represent significant progress in the real-time reconstruction of single-pixel imaging.

The quality of underwater imaging is greatly impacted by the scattering and absorption of light in turbid water environments. Single pixel imaging (SPI) has emerged as a promising solution for turbid underwater imaging, as it effectively suppresses the effects of scattering and is cost-effective due to the use of a single photodetector. However, the quality of SPI in highly turbid water is still unsatisfactory. To address this issue, we propose a novel computational framework for turbid water single-pixel imaging. The framework involves a machine learning-based polynomial regression fitting method, followed by data feature enhancement in the spectrum domain to obtain the rectified data, and ultimately, high-contrast image recovery. Furthermore, we propose a new metric, Edge Detection-based Enhancement Measure Evaluation (EDEME), to quantitatively evaluate the contrast of the recovered images. Our experimental results demonstrate that our proposed method can recover images in low turbidity water to a level comparable to clear water, and even in highly turbid water (turbidity greater than 50 NTU), the recovered images are legible with significantly improved EDEME values. Additionally, our method exhibits wide adaptability, requires minimal data operations, and outperforms some post-image processing methods. This work has significant implications for imaging, inspections, search and rescue, resource exploitation, and other applications in underwater environments.

Real-time computational ghost imaging (CGI) has received significant attention in recent years to overcome the trade-off between long acquisition time and high reconstructed image quality of CGI. Inspired by compound eyes, we propose a parallel computational ghost imaging with modulation patterns multiplexing and permutation to achieve a faster and high-resolution CGI. With modulation patterns multiplexing and permutation, several small overlapping fields-of-view can be obtained; meanwhile, the difficulty in alignment of illumination light field and multiple detectors can be well resolved. The method combining compound eyes with multi-detectors to capture light intensity can resolve the issue of a gap between detector units in the array detector. Parallel computation facilitates significantly reduced acquisition time, while maintaining reconstructed quality without compromising the sampling ratio. Experiments indicate that using m × m detectors reduce modulation pattern count, projector storage, and projection time to around 1/m2 of typical CGI methods, while increasing image resolution to m2 times. This work greatly promotes the practicability of parallel computational ghost imaging and provides optional solution for real-time computational ghost imaging.