We propose a convolutional neural network (CNN) based method, namely phase diversity convolutional neural network (PD-CNN) for the speed acceleration of phase-diversity wavefront sensing. The PD-CNN has achieved a state-of-the-art result, with the inference speed about $0.5$ ms, while fusing the information of the focal and defocused intensity images. When compared to the traditional phase diversity (PD) algorithms, the PD-CNN is a light-weight model without complicated iterative transformation and optimization process. Experiments have been done to demonstrate the accuracy and speed of the proposed approach.

We present a high-speed deep learning-based phase retrieval approach for Shack–Hartmann wavefront sensors used in astronomical adaptive optics. It reconstructs the Zernike modal coefficients from the image captured by the wavefront sensor with a lightweight convolutional neural network. Compared to the traditional slope-based wavefront reconstruction, the proposed approach uses the image captured by the sensor directly as inputs for more high-order aberrations. Compared to the recently developed iterative phase retrieval methods, the speed is much faster with the computation time less than 1 ms for a 100-aperture configuration, which may satisfy the requirement of an astronomical adaptive optics system. Simulations have been done to demonstrate the advantages of this approach. Experiments on a 241-unit deformable-secondary-mirror AOS have also been done to validate the proposed approach.

Monitoring space objects is crucial for space situational awareness, yet reconstructing 3D satellite models from ground-based telescope images is super challenging due to atmospheric turbulence, long observation distances, limited viewpoints, and low signal-to-noise ratios. In this paper, we propose a novel computational imaging framework that overcomes these obstacles by integrating a hybrid image pre-processing pipeline with a joint pose estimation and 3D reconstruction module based on controlled Gaussian Splatting (GS) and Branch-and-Bound (BnB) search. We validate our approach on both synthetic satellite datasets and on-sky observations of China's Tiangong Space Station and the International Space Station, achieving robust 3D reconstructions of low-Earth orbit satellites from ground-based data. Quantitative evaluations using SSIM, PSNR, LPIPS, and Chamfer Distance demonstrate that our method outperforms state-of-the-art NeRF-based approaches, and ablation studies confirm the critical role of each component. Our framework enables high-fidelity 3D satellite monitoring from Earth, offering a cost-effective alternative for space situational awareness. Project page: https://ai4scientificimaging.org/3DSatellites.

Context. Strong atmospheric turbulence has been a challenge for high-resolution imaging of solar telescopes. Adaptive optics (AO) systems are capable of improving the quality of imaging by correcting partial aberrations. Thus, the performance of Shack-Hartmann sensors in measuring aberrations generally determines the upper performance bound of AO systems. In solar AO, classic correlation Shack-Hartmann sensors only correct a small number of modal aberrations. Moreover, strong aberrations are difficult to measure stably by correlation Shack-Hartmann. In this context, the improvement in the performance of Shark-Hartmann sensors promises to enable higher-resolution imaging of extended objects for ground-based telescopes or Earth observation. Aims. We propose a new extended scene deep-phase-retrieval Shack-Hartmann wavefront sensing approach to improve the image quality of solar telescopes. It is capable of achieving high-accuracy measurements of high-spatial-resolution wavefronts on extended scene wavefront sensing. Moreover, it has great generalization when observing unknown objects from different fields of view of the telescope. Methods. Our proposed approach can extract features resembling the sub-aperture point spread function (PSF) from a Shack-Hartmann sensor image without any prior information. Then a convolutional neural network is used to establish a nonlinear mapping between the feature image and the wavefront modal coefficients. The extracted feature greatly eliminates the shape information of the extended object while maintaining more information related to aberrations. We verified the performance of the proposed method through simulations and experiments. Results. In the indoor experiment on the ground layer adaptive optics (GLAO) of the 1 m New Vacuum Solar Telescope, compared to the Shack-Hartmann correlation method, the proposed method reduces the correction errors by more than one third. When observing objects from different fields of view in the GLAO that differ from the object in the training data, the relative errors fluctuate within the range of 20% to 26%. The AO system with the proposed wavefront measurement method can obtain higher-resolution focal images of the simulated solar granulation after a round of offline correction. The average latency of the proposed method is about 0.6 ms.

Shack-Hartmann wavefront sensor (SHWFS) is the most popular wavefront sensor in adaptive optics systems. The Deep-Phase-Retrieval Wavefront Reconstruction (DPRWR) method, which was proposed by our group previously, is a kind of deep learning-based wavefront reconstruction method. It can extract more information from the SHWFS images to accurately obtain more Zernike mode coefficients. However, the application limits, performance upper bound, and noise immunity have not been investigated in detail in previous reports. In this paper, sub-aperture spot sampling, bit depth, number of reconstructed mode coefficients, and noise intensities are analyzed by simulations and experiments to investigate the influence of changes in these parameters on the performance of DPRWR. This work aims to optimize the configuration of DPRWR for better measurement accuracy, spatial resolution, and robustness.

Motion alignment is a critical task in satellite video super-resolution. Most existing methods rely on optical flow or deformable convolution for motion alignment. However, these methods perform poorly in handling complex satellite video scenes and diverse moving objects. To address this challenge, we propose an efficient super-resolution method based on a multitemporal difference and dynamic optimization framework. Specifically, we introduce a multilevel temporal difference analysis mechanism to rapidly capture comprehensive motion information. Based on the extracted temporal difference data, we further develop a temporal differences-guided dynamic routing optimization module (T-DROM) to extract multiscale motion information. In addition, we introduce a multiattention enhancement and correction module to refine the long-term temporal difference features from T-DROM and reduce accumulated errors. These enhancements help recover spatial details and improve spatio-temporal consistency. We conducted detailed ablation studies to validate our contributions and compared the proposed method with state-of-the-art video super-resolution approaches. Experimental results show that our method improves video reconstruction quality while achieving an effective balance between performance and efficiency.