(1) High-sensitivity detector technology

Quantum dot detector: Quantum dot materials have characteristics such as adjustable band gap and high absorption coefficient. The application of quantum dot detectors in night vision goggles can effectively improve the detection sensitivity of weak light (including infrared and visible light). For example, cadmium sulfide (CdS) quantum dot detectors can convert optical signals into electrical signals more efficiently in low-light environments. Compared with traditional detectors, they have a higher responsiveness to light and can capture targets in darker environments.

Superconducting nanowire detector (SNSPD): This detector is based on the characteristics of superconducting materials and can respond to single photons when working in low-temperature environments. It has extremely high sensitivity and extremely fast response speed, which can significantly improve the imaging ability of night vision goggles under extremely low light conditions. For example, in scenes such as astronomical observation or special military reconnaissance that require extremely weak light signals, SNSPD can help night vision goggles detect long-distance, extremely weak light, thereby obtaining a clearer target image.

(2) Advanced optical materials and coating technology

Chalcogenide glass optical materials: Chalcogenide glass has excellent transmittance in the infrared band and can be used in the optical system of night vision goggles, such as lenses and window materials. Compared with traditional optical glass, chalcogenide glass can reduce the absorption and scattering of infrared light in optical components, allowing more light to reach the detector, thereby improving the brightness and clarity of the image. For example, germanium-arsenic-sulfur (Ge-As-S) glass materials can be used to manufacture optical components that are sensitive to long-wave infrared, effectively improving the performance of night vision goggles in thermal imaging.

Multilayer anti-reflection coating technology: By coating multiple layers of thin films with different refractive indices on the surface of optical components, the reflection loss of light on the lens surface can be significantly reduced. This technology can increase the transmittance of light, especially in complex lighting environments, such as in the presence of moonlight, starlight or artificial light reflections, reduce glare and ghosting, and improve image quality. For example, optical lenses with alternating coatings of titanium dioxide (TiO₂) and silicon dioxide (SiO₂) with nanometer thickness can effectively reduce reflections and make images clearer.

(3) Optimization of digital image processing algorithms

Deep learning image denoising algorithms: Using deep learning technology, neural networks are trained with a large amount of image data, so that the algorithm can automatically identify and remove noise from images. Compared with traditional denoising algorithms, deep learning algorithms can more accurately separate noise and useful image information, effectively reducing noise while maintaining image details. For example, convolutional neural network (CNN)-based denoising algorithms can process noisy images collected by night vision goggles. The trained network can intelligently restore the real details of the image and improve the clarity and contrast of the image.

Super-resolution reconstruction algorithms: These algorithms can reconstruct high-resolution images from low-resolution images. During the imaging process of night vision goggles, due to the limitations of light conditions or detector resolution, images with lower resolution may be obtained. Super-resolution reconstruction algorithms use the prior knowledge of images and the correlation between multiple frames to improve the spatial resolution of images through interpolation, reconstruction and other methods. For example, super-resolution reconstruction algorithms based on sparse representation can improve low-resolution night vision images to higher resolutions, allowing observers to see more details.

(4) Adaptive optics technology

Integration of wavefront sensor and corrector: The wavefront sensor in the adaptive optics system can measure the wavefront distortion of light during propagation in real time, such as distortion caused by atmospheric turbulence, optical system aberrations, etc. Then, the corrector adjusts the optical system in real time based on the feedback information from the wavefront sensor to compensate for these distortions. The application of adaptive optics technology in night vision goggles can effectively reduce image blur and deformation caused by atmospheric jitter or imperfect optical components. For example, in the application scenarios of long-distance observation or airborne night vision goggles in flight, adaptive optics technology can improve the stability and clarity of imaging.

Optimization of the zoom optical system: By adopting new zoom optical materials and designs, a faster and more accurate zoom function can be achieved. During the use of night vision goggles, the focus can be quickly adjusted according to the distance and size of the target, so that the target always maintains a clear imaging state. At the same time, the optical parameters during the zoom process are optimized to reduce image jitter and resolution loss during zooming, and improve image quality. For example, the zoom system using liquid lens technology can achieve focus adjustment within milliseconds and maintain good imaging performance throughout the zoom range.