Long-range Fourier ptychographic imaging of the dynamic object with a single camera.

Fourier ptychographic imaging technology is a new imaging method proposed in recent years. This technology captures multiple low-resolution images, and synthesizes them into a high-resolution image in the Fourier domain by a phase retrieval algorithm, breaking through the diffraction limit of the lens. In the field of macroscopic Fourier ptychographic imaging, most of the existing research generally focus on high-resolution imaging of static objects, and applying Fourier ptychographic imaging technology to dynamic objects is a hot research area now. At present, most of the researches are to use camera arrays combined with multiplexed lighting, deep learning or other algorithms, but the implementation of these methods is complicated or costly. Based on the diffraction theory of Fourier optics, this paper proposes that by expanding and focusing the illumination area, we can apply Fourier ptychographic imaging technology with a single camera to moving objects within a certain range. Theoretical analysis and experiments prove the feasibility of the proposed method. We successfully achieve high-resolution imaging of the dynamic object, increasing the resolution by about 2.5 times. This paper also researches the impact of speckles in the illuminated area on imaging results and proposes a processing method to reduce the impact of speckles.

Single-pixel tracking of fast-moving object using geometric moment detection.

Real-time tracking of fast-moving object have many important applications in various fields. However, it is a great challenge to track of fast-moving object with high frame rate in real-time by employing single-pixel imaging technique. In this paper, we present the first single-pixel imaging technique that measures zero-order and first-order geometric moments, which are leveraged to reconstruct and track the centroid of a fast-moving object in real time. This method requires only 3 geometric moment patterns to illuminate a moving object in one frame. And the corresponding intensities collected by a single-pixel detector are equivalent to the values of the zero-order and first-order geometric moments. We apply this new approach of measuring geometric moments to object tracking by detecting the centroid of the object in two experiments. The root mean squared errors in the transverse and axial directions are 5.46 pixels and 5.53 pixels respectively, according to the comparison of data captured by a camera system. In the second experiment, we successfully track a moving magnet with a frame rate up to 7400 Hz. The proposed scheme provides a new method for ultrafast target tracking applications.