A phased sparse aperture system provides an economic solution to get high resolution images with less volume and weight. The crucial point of such systems is adaptive correction of piston, that is, a close-loop control aiming at stabilizing the optical path differences within a fraction of the wavelength. In this paper, we present an autonomous phasing approach using stochastic parallel gradient descent algorithm through optimization of image quality. The synthetic system can be phased by iteratively commanding piston actuators without any additional optics. Simulations are first performed to test the validity. Then experimental results based on a binocular telescope testbed are presented, showing that our proposed close-loop control of piston correction doesn’t only work with both laser and white-light point sources, but also with an extended object.

The pistons of sparse aperture systems need to be controlled within a fraction of a wavelength for the system’s optimal imaging performance. In this paper, we demonstrate that deep learning is capable of performing piston sensing with a single wide-band image after appropriate training. Taking the sensing issue as a fitting task, the deep learning-based method utilizes a deep convolutional neural network to learn complex input-output mapping relations between the broadband intensity distributions and corresponding piston values. Given a trained network and one broadband focal intensity image as the input, the piston can be obtained directly and the capture range achieving the coherence length of the broadband light is available. Simulations and experiments demonstrate the validity of the proposed method. Using only in-focused broadband images as the inputs without defocus division and wavelength dispersion, obviously relaxes the optics complexity. In view of the efficiency and superiority, it’s expected that the method proposed in this paper may be widely applied in multi-aperture imaging.

In this paper, an improved method of measuring wavefront aberration based on image with machine learning is proposed. This method had better real-time performance and higher estimation accuracy in free space optical communication in cases of strong atmospheric turbulence. We demonstrated that the network we optimized could use the point spread functions (PSFs) at a defocused plane to calculate the corresponding Zernike coefficients accurately. The computation time of the network was about 6–7 ms and the root-mean-square (RMS) wavefront error (WFE) between reconstruction and input was, on average, within 0.1263 waves in the situation of D/r0 = 20 in simulation, where D was the telescope diameter and r0 was the atmospheric coherent length. Adequate simulations and experiments were carried out to indicate the effectiveness and accuracy of the proposed method.

The next generation of optical telescopes will provide high-resolution imaging of celestial objects by using the aperture synthesis technique. To preserve the quality of the image, fast corrections of the pistons among subapertures have to be applied, namely, the co-phasing of the array. The image-based co-phasing method via an optimization procedure has been newly developed. Despite simplicity and strong commonality, when dealing with large piston errors, this correction method is also faced with a problem in which the metric function easily falls into the local convergence, especially in the case of broadband imaging with many subapertures. In this study, an improved stochastic parallel gradient descent (SPGD) algorithm based on heuristic search is proposed for co-phasing, termed the metaheuristic SPGD algorithm. The heuristic research scheme assists the original SPGD algorithm in getting rid of local extrema. By iterations of this algorithm, the synthetic system can be co-phased without any additional instruments and operations. The effectiveness of the proposed algorithm is verified by means of simulation. Given the efficiency and superiority, it is expected that the method proposed in this study may find wide applications in multi-aperture imaging.

Flat optics has been considered promising for constructions of spaceborne imaging systems with apertures in excess of 10 m. Despite recent advances, there are long-existing challenges to perform in-phase stitching of multiple flat optical elements. Phasing the segmented planar instrument has remained at the proof of concept. Here, we achieve autonomous system-level cophasing of a 1.5-m stitching flat device, bridging the gap between the concept and engineering implementation. To do so, we propose a flat element stitching scheme, by manipulating the point spread function, which enables our demonstration of automatically bringing seven flat segments’ tip/tilt and piston errors within the tolerance. With phasing done, the 1.5-m system has become the largest phased planar instrument ever built in the world, to our knowledge. The first demonstration of phasing the large practical flat imaging system marks a significant step towards fielding a 10-m class one in space, also paving the way for ultrathin flat imaging in various remote applications.

Optical sparse aperture imaging shows great promise for the next generation of high resolution systems. In this paper, we propose and demonstrate an active sparse aperture imaging approach using independent transmitter modulation to digitally overcome phasing errors, correct aberrations, and further improve resolution. The reported imaging scheme consists of a general sparse aperture system and an active illumination unit, specifically an independent pattern projector. A series of raw images are captured with the projector scanned to illuminate the object. Based on the acquired data set, the improved incoherent Fourier ptychographic algorithm is utilized to reconstruct sparse aperture images with distortions removed and contrast enhanced. Furthermore, thanks to illumination pattern modulation, higher resolution beyond the diffraction limit of the synthetic aperture system is gained as a benefit. Good-quality and higher-resolution sparse aperture imagery obtained by employing our proposed technique in both simulation and experiment demonstrates the effectiveness. The reported approach may provide new insights to address the phasing and image restoration problems of sparse aperture systems in the transmitting path rather than only in the receiving path.