The spatial distribution of higher-order Hermite–Gaussian (HG) modes is more complicated than the fundamental mode, and the characteristics of different modes and their orthogonal characteristics have essential applications in the fields of measurement, imaging, and large-capacity communications. The main issue in future applications is how to efficiently generate higher-order HG modes. The spatial light modulator has high spatial resolution and excellent maneuverability, so it is a simple and effective way to generate higher-order HG modes. Here, the efficient generation of higher-order HG modes via cascaded spatial light modulators was experimentally investigated. For the HG90 mode, the conversion efficiency is 61%. The method with high conversion efficiency has promising application potentials in biophotonics, laser physics, and quantum information.

Laser technology has made rapid progress in recent years and has been widely used in various fields such as medicine, biology, military, and materials science. However, the limitations of traditional Gaussian intensity distribution of the laser beams in applications have prompted the emergence and development of flat-top beam shaping technology, which has received widespread attention. Here, we introduce a new method for generating flat-top beams that combines the traditional Gerchberg-Saxton algorithm with convolutional neural networks, using spatial light modulators to achieve flat-top beam shaping. A comparative analysis was conducted by comparing the root mean square error and diffraction efficiency of the generated flat-top beam with the results obtained using only the traditional Gerchberg-Saxton algorithm. Compared with the traditional Gerchberg-Saxton algorithm, the method proposed in this paper can generate a flat-top beam with smaller differences from the target light intensity and higher energy utilization, providing new possibilities for the application of laser technology.

Phase retrieval is a critical technique for reconstructing optical field information. By reconstructing the phase of the light field from intensity measurements, this approach effectively address phase distortions induced by atmospheric turbulence and optical system imperfections, substantially enhancing the quality and multidimensional information content of remote sensing imagery, thereby providing critical support for disaster monitoring, resource exploration, and climate research. In recent years, the rapid development of deep learning has offered unprecedented support for computational optical imaging. However, traditional deep learning techniques in phase retrieval often rely on large sample datasets and deep network models, which pose numerous challenges in practical applications. To address this issue, this paper proposes a novel lightweight network, StarNet. Results of numerical simulations demonstrate that StarNet exhibits superior performance in handling higher-order modes and arbitrary intensity optical fields under random phase perturbations. It can effectively extract phase distortion information under few-shot conditions and accurately recover the intensity of the optical field. This few-shot phase-retrieval algorithm not only paves the way for practical advancements in computational imaging but also offers new approaches into data-scarce computational problems, such as remote sensing imaging and signal processing.

Due to its complex spatial distribution, the higher-order Hermite–Gaussian mode possesses significant application in fields such as precision measurement and optical communication. The spatial light modulator, with its capability to modulate the complex amplitude distribution of the incident light field, finds extensive applications in optical information processing and adaptive optics, thus making it an indispensable tool in these fields. Using cascaded spatial light modulators can efficiently and superbly generate a higher-order Hermite–Gaussian mode; however, the experimental system is challenging, and there are many influencing factors, such as the misalignment between the optical field on the plane of the second spatial light modulator and the hologram loaded onto it, as well as the spot size of the optical field on the plane of the second spatial light modulator. In this paper, we analyzed the influence of the above factors on the quality of generating a higher-order Hermite–Gaussian mode, providing a reference for the efficient and high-quality generation of the higher-order Hermite–Gaussian mode.

The scattering of light induced by hazy conditions severely degrades imaging quality, leading to reduced contrast, loss of detail, and blurring. These issues present severe challenges to critical fields such as autonomous driving, remote sensing, and object detection. Traditional Stokes vector-based dehazing algorithms are particularly susceptible to noise interference in polarized images, which considerably hampers their dehazing performance under dense hazy conditions. To address these limitations, this paper proposes a polarization-based dehazing approach that integrates the PBM3D denoising algorithm with the Stokes vector. This method effectively separates scattered light from target reflected light while suppressing noise and enhancing image details and clarity. Experimental results demonstrate that in real-world dense hazy conditions, the proposed method significantly outperforms traditional Stokes vector-based dehazing algorithms. This study not only enhances imaging quality in hazy conditions but also lays a foundation for future applications in extreme hazy conditions across domains such as autonomous driving and environmental monitoring.