We propose and demonstrate a computational imaging technique that uses structured illumination based on a two-dimensional discrete cosine transform to perform imaging with a single-pixel detector. A scene is illuminated by a projector with two sets of orthogonal patterns, then by applying an inverse cosine transform to the spectra obtained from the single-pixel detector a full-colour image is retrieved. This technique can retrieve an image from sub-Nyquist measurements, and the background noise is easily cancelled to give excellent image quality. Moreover, the experimental set-up is very simple.

In the magnetically suspended momentum wheel, the orientation of the rotor shaft can be actively changed to generate 2-D torques. An adaptive back-stepping sliding mode control (SMC) method is proposed to precisely control the deflection angles. First, the magnetic torques generated by the magnetic bearings are analyzed. According to the magnetic torques, coupled torques exist and influence the tracking control of the deflection angles. It is difficult to model the coupled torque disturbances because their relationship with translations, deflection angles, currents, and other parameters is complex. By analyzing the dynamics and the magnetic torques, the tracking error dynamic is modeled as a system with unmodeled disturbances and parameter uncertainties. A back-stepping integral sliding mode controller whose sliding surface considers the integrals of the angle error and the tracking error is designed for this system. The switching gain should be relatively large to ensure system stability. This will induce severe chattering, which hinders the precision of the deflection angle. A fuzzy algorithm whose inputs include the sliding value and deflection angle is designed to adaptively tune the switching gain of the sliding mode method. According to the simulation and experimental results, the proposed method improves tracking performances and reduces chattering.

Image-based target tracking methods rely on continuous image acquisition and post-processing, which will result in low tracking efficiency. To realize real-time tracking of fast moving objects, we propose an image-free target tracking scheme based on the discrete cosine transform and single-pixel detection. Our method avoids calculating all the phase values, so the number of samples can be greatly reduced. Furthermore, complementary modulation is applied to reduce the measurement noise, and background subtraction is applied to enhance the contrast. The results of simulations and experiments demonstrate that the proposed scheme can accomplish the tracking task in a complex background with a sampling ratio of less than 0.59% of the Nyquist-Shannon criterion, thereby significantly reducing the measurement time. The tracking speed can reach 208 fps at a spatial resolution of 128 × 128 pixels with a tracking error of no more than one pixel. This technique provides a new idea for real-time tracking of fast-moving targets.

Computational ghost imaging (CGI) captures images via the correlation between a set of illumination patterns and the transmitted/reflected signal of an object. It has been widely studied in many fields and has advanced from experimental verification to practical applications. However, there will be some motion blur in the results when a moving object is imaged with an insufficiently fast detector. To eliminate this blurring, we present here a tracking compensation method for CGI, in which a series of patterns illuminate the object according to the motion of the object, and the signal intensity is collected synchronously by the bucket detector. The principle of this compensation for moving and rotating objects is explained in detail. Both simulation and experimental results show that this method can effectively eliminate the motion blur and provide high quality reconstruction, outperforming conventional CGI, broad potential applications in object tracking, remote sensing and real-time imaging.

Motion blur restoration is essential for the imaging of moving objects, especially for single-pixel imaging (SPI), which requires multiple measurements. To reconstruct the image of a moving object with multiple motion modes, we propose a novel motion blur restoration method of SPI using geometric moment patterns. We design a novel localization method that uses normalized differential first-order moments and central moment patterns to determine the object’s translational position and rotation angle information. Then, we perform motion compensation by using shifting Hadamard patterns. Our method effectively improves the detection accuracy of multiple motion modes and enhances the quality of the reconstructed image. We perform simulations and experiments, and the results validate the effectiveness of the proposed method.

With the emerging development of hyperspectral single-pixel imaging (SPI) systems, the trade-off between the simplicity of optical structure and the correction of chromatic aberration is now an essential factor to be considered. To address both issues simultaneously, we propose a chromatic-aberration-corrected hyperspectral single-pixel imaging scheme, which is based on spectral measurement and dispersion correction. Its achromatism feature is evaluated by optical simulations and proof-of-concept experiments. Moreover, to overcome the shortcomings of traditional algorithms, a new adaptive iterative algorithm is employed, which can further optimize image quality. The results demonstrate that both dispersion and noise in our system are significantly reduced. Taking the position coordinate variance as a figure of merit, we have realized an order of magnitude improvement in the lateral chromatic aberration over the spectral range of 400–780 nm compared to that in conventional hyperspectral SPI. Meanwhile, the contrast-to-noise ratio in our system is enhanced on average by 3 dB. To the best of our knowledge, this is the first such demonstration, and the technique presents possibilities for future integrated applications of high spatial/spectral resolution over the entire visible range, and the system has the potential to be scaled down for future integrated applications.