Hao Chen

In this Letter, we present a centroid detection method based on a Multi-pixel Photon Counter (MPPC) array and an intensity spatial distribution modulator array. This method is adept at efficiently determining the two-dimensional centroid position, even when only a small number of photons are detected. We have integrated every single-point MPPC with an intensity spatial distribution modulator, which enhances its ability to discern positions. Only two units are required to detect the two-dimensional centroid position of the spot. Additionally, a third unit can be used to monitor the overall intensity flicker of the spot, which significantly enhances the stability and robustness of the detection. Under 500 root mean square (RMS) photons detected, the sensor consistently extracts the two-dimensional centroid with an impressive frame rate of 30 kHz and a root mean square error (RMSE) of 7.11 μm. These results demonstrate a promising approach that could significantly improve the detection of faint targets with high sensitivity while still maintaining a fast frame rate. Therefore, this method has the potential to enhance the detection capability of tilt aberrations, thereby providing the foundation of the adaptive optics system for the coming extremely large telescopes.

The publisher's note contains a correction to [ Opt. Express 31 , 10320 ( 2023 ) 10.1364/OE.481433 ]. The article was corrected on 7 November 2025.

Shack-Hartmann-based wavefront sensing combined with deep learning, due to its fast, accurate, and large dynamic range, has been widely studied in many fields including ocular aberration measurement. Problems such as noise and corneal reflection affect the accuracy of detection in practical measuring ocular aberration systems. This paper establishes a framework comprising of a noise-added model, Hartmannograms with corneal reflections and the corneal reflection elimination algorithm. Therefore, a more realistic data set is obtained, enabling the convolutional neural network to learn more comprehensive features and carry out real machine verification. The results show that the proposed method has excellent measurement accuracy. The root mean square error (RMSE) of the residual wavefront is 0.00924 ± 0.0207λ (mean ± standard deviation) in simulation and 0.0496 ± 0.0156λ in a real machine. Compared with other methods, this network combined with the proposed corneal reflection elimination algorithm is more accurate, speedier, and more widely applicable in the noise and corneal reflection situations, making it a promising tool for ocular aberration measurement.

This paper proposes a supervised deep neural network model for accomplishing highly efficient image quality assessment (IQA) for adaptive optics (AO) images. The AO imaging systems based on ground-based telescopes suffer from residual atmospheric turbulence, tracking error, and photoelectric noise, which can lead to varying degrees of image degradation, making image processing challenging. Currently, assessing the quality and selecting frames of AO images depend on either traditional IQA methods or manual evaluation by experienced researchers, neither of which is entirely reliable. The proposed network is trained by leveraging the similarity between the point spread function (PSF) of the degraded image and the Airy spot as its supervised training instead of relying on the features of the degraded image itself as a quality label. This approach is reflective of the relationship between the degradation factors of the AO imaging process and the image quality and does not require the analysis of the image’s specific feature or degradation model. The simulation test data show a Spearman’s rank correlation coefficient (SRCC) of 0.97, and our method was also validated using actual acquired AO images. The experimental results indicate that our method is more accurate in evaluating AO image quality compared to traditional IQA methods.

Atmospheric dispersion is a wavelength-dependent effect that affects astronomical observations performed using ground-based telescopes. The effect of atmospheric dispersion for a 4-meter telescope is analyzed and an atmospheric dispersion corrector (ADC) system was designed for its adaptive optical system. With the help of adaptive optical system and a line fit method, the science images can be conveniently used to measure the atmospheric dispersion and to control ADC to correct the dispersion, and thus the full-width at half-maximum of the final images can approach the diffraction limit of the telescope.

Traditional plenoptic wavefront sensors (PWS) suffer from the obvious step change of the slope response which leads to the poor performance of phase retrieval. In this paper, a neural network model combining the transformer architecture with the U-Net model is utilized to restore wavefront directly from the plenoptic image of PWS. The simulation results show that the averaged root mean square error (RMSE) of residual wavefront is less than 1/14λ (Marechal criterion), proving the proposed method successfully breaks through the non-linear problem existed in PWS wavefront sensing. In addition, our model performs better than the recently developed deep learning models and traditional modal approach. Furthermore, the robustness of our model to turbulence strength and signal level is also tested, proving the good generalizability of our model. To the best of our knowledge, it is the first time to perform direct wavefront detection with a deep-learning-based method in PWS-based applications and achieve the state-of-the-art performance.

Traditional plenoptic wavefront sensors (PWFS) suffer from the obvious step change of the slope response, leading to poor wavefront detection performance. In order to solve this problem, in this paper, a deep learning model is proposed to restore phase maps directly from slope measurements of PWFS. Numerical simulations are employed to demonstrate our approach, and the statistical residual wavefront root mean square error (RMSE) of our method is 0.0810 ± 0.0258λ, which is much superior to those of modal algorithm (0.2511 ± 0.0587λ) and zonal approach (0.3584 ± 0.0487λ). The internal driving force of PWFS-ResUnet is investigated, and the slope response differences between sub-apertures and directions are considered as a probably key role to help our model to accurately restore the phase map. Additionally, the robustness of our model to turbulence strength and signal-to-noise ratio (SNR) level is also tested. The proposed method provides a new direction to solve the nonlinear problem of traditional PWFS.