With the successful application of the Shack–Hartmann wavefront sensor in measuring aberrations of the human eye, researchers found that, when the aberration is large, the local wavefront distortion is large, and it causes the spot corresponding to the sub-aperture of the microlens to shift out of the corresponding range of the sub-aperture. However, the traditional wavefront reconstruction algorithm searches for the spot within the corresponding range of the sub-aperture of the microlens and reconstructs the wavefront according to the calculated centroid, which leads to wavefront reconstruction errors. To solve the problem of the small dynamic range of the Shack–Hartmann wavefront sensor, this paper proposes a wavefront reconstruction algorithm based on the autocorrelation method and a neural network. The autocorrelation centroid extraction method was used to calculate the centroid in the entire spot map in order to obtain a centroid map and to reconstruct the wavefront by matching the centroid with the microlens array through the neural network. This method breaks the limitation of the sub-aperture of the microlens. The experimental results show that the algorithm improves the dynamic range of the first 15 terms of the Zernike aberration reconstruction to varying degrees, ranging from 62.86% to 183.87%.

The precision in evaluating global ambient light profoundly impacts the performance of image-dehazing technologies. Many approaches for quantifying atmospheric light intensity suffer from inaccuracies, leading to a decrease in dehazing effectiveness. To address this challenge, we introduce an approach for estimating atmospheric light based on the polarization contrast between the sky and the scene. By employing this method, we enhance the precision of atmospheric light estimation, enabling the more accurate identification of sky regions within the image. We adapt the original dark channel dehazing algorithm using this innovative technique, resulting in the development of a polarization-based dehazing imaging system employed in practical engineering applications. Experimental results reveal a significant enhancement in the accuracy of atmospheric light estimation within the dark channel dehazing algorithm. Consequently, this method enhances the overall perceptual quality of dehazed images. The proposed approach demonstrates a 28 percent improvement in SSIM and a contrast increase of over 20 percent when compared to the previous method. Additionally, the created dehazing system exhibits real-time processing capabilities.

In this paper, we demonstrate the use of the modified phase retrieval as a method for application in the measurement of small-slope free-form optical surfaces. This technique is a solution for the measurement of small-slope free-form optical surfaces, based on the modified phase retrieval algorithm, whose essence is that only two defocused images are needed to estimate the wave front with an accuracy similar to that of the traditional phase retrieval but with less image capturing and computation time. An experimental arrangement used to measure the small-slope free-form optical surfaces using the modified phase retrieval is described. The results of these experiments demonstrate that the modified phase retrieval method can achieve measurements comparable to those of the standard interferometer.

Complex optical elements have the advantages of improving image quality and optical performance and expanding the field of view. Therefore, it is widely used in X-ray scientific devices, adaptive optical elements, high-energy laser systems, and other fields and is a hot research direction in precision optics. Especially for precision machining, there is a greater need for high-precision testing technology. However, how to measure complex surfaces efficiently and accurately is still an important research topic in optical metrology technology. In order to verify the ability of optical metrology for complex optical surfaces with wavefront sensing based on image information of the focal plane, some experiment platforms in different types of optical surfaces were set up. In order to validate the feasibility and validity of wavefront-sensing technology based on image information of focal planes, a large number of repetitive experiments were carried out. The measurement results with wavefront sensing based on image information of the focal plane were compared with the measurement results with the ZYGO interferometer. The experimental results demonstrate that good agreement is obtained among the error distribution, PV value, and RMS value of the ZYGO interferometer, which shows the feasibility and validity of wavefront sensing based on image information of focal plane technology in optical metrology for the complex optical surface.

Space object recognition in high Earth orbits (between 2000 km and 36,000 km) is affected by moonlight and clouds, resulting in some bright or saturated image areas and uneven image backgrounds. It is difficult to separate dim objects from complex backgrounds with gray thresholding methods alone. In this paper, we present a segmentation method of star images with complex backgrounds based on correlation between space objects and one-dimensional (1D) Gaussian morphology, and the focus is shifted from gray thresholding to correlation thresholding. We build 1D Gaussian functions with five consecutive column data of an image as a group based on minimum mean square error rules, and the correlation coefficients between the column data and functions are used to extract objects and stars. Then, lateral correlation is repeated around the identified objects and stars to ensure their complete outlines, and false alarms are removed by setting two values, the standard deviation and the ratio of mean square error and variance. We analyze the selection process of each thresholding, and experimental results demonstrate that our proposed correlation segmentation method has obvious advantages in complex backgrounds, which is attractive for object detection and tracking on a cloudy and bright moonlit night.

Achromatic metalenses face stringent aperture and numerical aperture (NA) constraints, which have become a key bottleneck in metasurface imaging. To this end, a novel achromatic imaging method was first proposed, utilizing the unique wideband consistency of diffracted Bessel spots combined with non-blind image restoration techniques. To address the off-axis aberration issue, off-axis achromatic meta-axicons with eccentric conical phases were further designed to convert oblique plane waves into wideband uniform off-axis Bessel beams. Ultimately, a single metasurface integrating 9 meta-axicons with different design field angles was developed, and a meta-camera was constructed accordingly. After image restoration, the meta-camera achieves achromatic imaging within a 10° stitched field of view (FOV), and an angular resolution close to that of near-diffraction-limit lens with the same aperture throughout the entire FOV. The core idea of achieving achromatic imaging based on natural dispersion laws in this study enables the wideband minimalist optical system based on metasurface to completely circumvent the aperture limitation, providing a highly valuable solution for large-aperture meta-camera design that can simultaneously accommodate wideband and off-axis FOV.