标题： 学习透过耀斑观察 显示英文标题

标题： Learning to See Through Flare

摘要： 机器视觉系统容易受到激光眩光的影响，其中不必要的强激光照射会通过过饱和或传感器像素的永久损坏，使它对环境的感知失真或失明。 我们引入了NeuSee，这是第一个跨整个可见光谱的高保真传感器保护计算成像框架。 它联合学习了一个衍射光学元件（DOE）的神经表示和一个频域空间Mamba-GAN网络用于图像修复。 NeuSee系统在10万张独特的图像上进行对抗训练，以抑制高达$10^6$倍于传感器饱和阈值$I_{\textrm{sat}}$的峰值激光辐照度，这个阈值是相机传感器在没有DOE的情况下可能遭受损坏的点。 我们的系统利用异构数据和模型并行性进行分布式计算，整合高光谱信息和多个神经网络进行现实模拟和图像修复。 NeuSee考虑了开放世界场景中动态变化的激光波长、强度和位置，以及镜头眩光效应、未知的环境光照条件和传感器噪声。 它优于其他学习的DOEs，在首次实现全光谱成像和激光抑制方面，恢复图像质量提高了10.1%。 显示英文摘要

摘要： Machine vision systems are susceptible to laser flare, where unwanted intense laser illumination blinds and distorts its perception of the environment through oversaturation or permanent damage to sensor pixels. We introduce NeuSee, the first computational imaging framework for high-fidelity sensor protection across the full visible spectrum. It jointly learns a neural representation of a diffractive optical element (DOE) and a frequency-space Mamba-GAN network for image restoration. NeuSee system is adversarially trained end-to-end on 100K unique images to suppress the peak laser irradiance as high as $10^6$ times the sensor saturation threshold $I_{\textrm{sat}}$, the point at which camera sensors may experience damage without the DOE. Our system leverages heterogeneous data and model parallelism for distributed computing, integrating hyperspectral information and multiple neural networks for realistic simulation and image restoration. NeuSee takes into account open-world scenes with dynamically varying laser wavelengths, intensities, and positions, as well as lens flare effects, unknown ambient lighting conditions, and sensor noises. It outperforms other learned DOEs, achieving full-spectrum imaging and laser suppression for the first time, with a 10.1\% improvement in restored image quality.