学习穿透激光眩视（Dazzle）感知环境
Learning to See Through Dazzle
AI总结：
针对激光眩视导致机器视觉失效的问题，本研究结合波前编码相位掩模与融合傅里叶特征的SGAN网络，实现强激光干扰下的高质量图像恢复，性能优于现有最优方法。
摘要：
机器视觉易受激光眩视影响，强激光会通过传感器像素的过饱和或永久性损伤致盲机器视觉，扭曲其对环境的感知。本研究采用波前编码相位掩模扩散激光能量，并提出三明治生成对抗网络（sandwich generative adversarial network, SGAN），用于从复杂图像退化中恢复图像——这类退化包括不同程度的激光诱导图像饱和、掩模诱导的图像模糊、未知光照条件以及各类噪声干扰。SGAN架构将两个GAN包裹在一个可学习的图像反卷积模块外，融合了判别式与生成式方法。此外，研究利用傅里叶特征表示降低神经网络的光谱偏差，提升其对高频图像细节的学习能力。端到端训练过程包含基于真实物理模型的大规模训练数据合成，数据源自公开可用图像。研究训练的SGAN可抑制高达传感器饱和阈值10^6倍的激光峰值辐照度——在无掩模的情况下，该辐照度水平可能导致相机传感器受损。训练后的模型在合成数据集与实验室采集数据上均接受了评估。在广泛的场景内容、激光功率、激光入射角度、环境光照强度及噪声特性条件下，所提出的图像恢复模型在定量与定性表现上均优于现有最优方法。
英文摘要：
Machine vision is susceptible to laser dazzle, where intense laser light can blind and distort its perception of the environment through oversaturation or permanent damage to sensor pixels. Here we employ a wavefront-coded phase mask to diffuse the energy of laser light and introduce a sandwich generative adversarial network (SGAN) to restore images from complex image degradations, such as varying laser-induced image saturation, mask-induced image blurring, unknown lighting conditions, and various noise corruptions. The SGAN architecture combines discriminative and generative methods by wrapping two GANs around a learnable image deconvolution module. In addition, we make use of Fourier feature representations to reduce the spectral bias of neural networks and improve its learning of high-frequency image details. End-to-end training includes the realistic physics-based synthesis of a large set of training data from publicly available images. We trained the SGAN to suppress the peak laser irradiance as high as $10^6$ times the sensor saturation threshold - the point at which camera sensors may experience damage without the mask. The trained model was evaluated on both a synthetic data set and data collected from the laboratory. The proposed image restoration model quantitatively and qualitatively outperforms state-of-the-art methods for a wide range of scene contents, laser powers, incident laser angles, ambient illumination strengths, and noise characteristics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
