Real-time wavefront correction is a challenging problem to present for conventional adaptive optics systems. Here, we present an all-optical system to realize real-time wavefront correction. Using deep learning, the system, which contains only multiple transmissive diffractive layers, is trained to realize high-quality imaging for unknown, random, distorted wavefronts. Once physically fabricated, this passive optical system is physically positioned between the imaging lens and the image plane to all-optically correct unknown, new wavefronts whose wavefront errors are within the training range. Simulated experiments showed that the system designed for the on-axis field of view increases the average imaging Strehl Ratio from 0.32 to 0.94, and the other system intended for multiple fields of view increases the resolvable probability of binary stars from 30.5% to 69.5%. Results suggested that DAOS performed well when performing wavefront correction at the speed of light. The solution of real-time wavefront correction can be applied to other wavelengths and has great application potential in astronomical observation, laser communication, and other fields.

With new artificial intelligence (AI) technologies and application scenarios constantly emerging, AI technology has become widely used in astronomy and has promoted notable progress in related fields. A large number of papers have reviewed the application of AI technology in astronomy. However, relevant articles seldom mention telescope intelligence separately, and it is difficult to understand the current development status of and research hotspots in telescope intelligence from these papers. This paper combines the development history of AI technology and difficulties with critical telescope technologies, comprehensively introduces the development of and research hotspots in telescope intelligence, conducts a statistical analysis of various research directions in telescope intelligence, and defines the merits of these research directions. A variety of research directions are evaluated, and research trends in each type of telescope intelligence are indicated. Finally, according to the advantages of AI technology and trends in telescope development, potential future research hotspots in the field of telescope intelligence are given.

Artificial intelligence technology has been widely used in astronomy, and new artificial intelligence technologies and application scenarios are constantly emerging. There have been a large number of papers reviewing the application of artificial intelligence technology in astronomy. However, relevant articles seldom mention telescope intelligence separately, and it is difficult to understand the current development status and research hotspots of telescope intelligence from these papers. This paper combines the development history of artificial intelligence technology and the difficulties of critical technologies of telescopes, comprehensively introduces the development and research hotspots of telescope intelligence, then conducts statistical analysis on various research directions of telescope intelligence and defines the research directions' merits. All kinds of research directions are evaluated, and the research trend of each telescope's intelligence is pointed out. Finally, according to the advantages of artificial intelligence technology and the development trend of telescopes, future research hotspots of telescope intelligence are given.

The increasing aperture of next-generation telescopes demands higher-resolution wavefront sensing. The sensing resolution of the Shack-Hartmann wavefront sensor (SHWFS), a key component in adaptive optics, is fundamentally limited by its sub-aperture density. To overcome this limitation, we introduce a frequency-domain filter-based neural network (FF-Net) for super-resolution wavefront reconstruction (SRWR). Inspired by the physical imaging principles of the SHWFS and the convolution theorem, FF-Net utilizes learnable Gabor filters to extract aberration features across various frequency bands. This approach enables more effective feature decoupling from aliased sub-aperture spots compared to standard convolutional kernels, leading to high-precision reconstruction. Numerical simulations demonstrate that FF-Net achieves state-of-the-art SRWR performance. Notably, with an under-sampled SHWFS, our method outperforms the low-order reconstruction accuracy of the conventional method employing four times the sub-aperture density, while simultaneously retrieving higher-order modes. Furthermore, its GPU-accelerated inference time of under 1 ms meets the real-time requirements of most astronomical adaptive optics systems. FF-Net demonstrates that physics-informed network design is a powerful strategy for developing more accurate, robust, and interpretable deep learning solutions in adaptive optics.