Towards Photography Through Realistic Fog

G. Satat, M. Tancik and R. Raskar, "Towards Photography Through Realistic Fog", IEEE International Conference on Computational Photography (ICCP), (2018).


 Imaging through fog has important applications in industries such as self-driving cars, augmented driving, airplanes, helicopters, drones and trains. Current solutions are based on radar that suffers from poor resolution (due to the long wavelength), or on time gating that suffers from low signal-to-noise ratio. Here we demonstrate a technique that recovers reflectance and depth of a scene obstructed by dense, dynamic, and heterogeneous fog. For practical use cases in self-driving cars, the imaging system is designed in optical reflection mode with minimal footprint and is based on LIDAR hardware. Specifically, we use a single photon avalanche diode (SPAD) camera that time-tags individual detected photons. A probabilistic computational framework is developed to estimate the fog properties from the measurement itself, and distinguish between background photons reflected from the fog and signal photons reflected from the target. The method is experimentally evaluated on a wide range of fog densities created in a fog chamber. The suggested approach demonstrates recovering objects 57cm away from the camera when the visibility is 37cm. In that case it recovers depth with a resolution of 5cm and scene reflectance with an improvement of 4dB in PSNR and  3.4X reconstruction quality in SSIM over time gating techniques.

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