用神经网络设计光学元件，防激光过曝并恢复图像。
Learning to See Through Flare
- 联合学习衍射光学元件与频域Mamba-GAN网络，实现端到端修复。
- 可抑制高达10⁶倍饱和阈值的激光强度，保护传感器不损坏。
- 首次实现全谱成像与激光抑制，适合自动驾驶等高危场景。
机器视觉系统易受激光耀斑影响，强光导致传感器过曝甚至永久损伤。本文提出NeuSee，首个跨全可见光谱的高保真传感器防护计算成像框架。它联合学习衍射光学元件（DOE）的神经表征与频域Mamba-GAN图像恢复网络，基于10万张独特图像端到端对抗训练，可抑制高达10⁶倍传感器饱和阈值（I_sat）的峰值激光辐照度，防止无DOE时的传感器损坏。系统融合异构数据与模型并行计算，结合高光谱信息与多神经网络，实现真实场景模拟与图像重建。考虑动态变化的激光波长、强度、位置、镜头眩光、未知环境光照及传感器噪声等开放世界因素。相比其他学习型DOE，NeuSee首次实现全谱成像与激光抑制，图像恢复质量提升10.1%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。