Compressed sensing theory allows for a high-resolution image recovery of sparse image data. However, image scene data from an RGB camera, captured at night-time or in fog, dust, or rainy conditions, is difficult to read. The fusion of IR camera data with RGB camera data allows us to determine the difference between objects in the scene and noise (dust, rain, fog). This paper demonstrates a compressive sensing methodology applied to the scattered image data of the RGB camera to determine objects or obstacles in a scene, which allows for low-cost solutions for the problem of autonomous driving in unfair imaging conditions.
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