  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.
