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Outdoor vision robotic systems and autonomous cars suffer from manyimage-quality issues, particularly haze, defocus blur, and motion blur, whichwe will define generically as "blindness issues". These blindness issues mayseriously affect the performance of robotic systems and could lead to unsafedecisions being made. However, existing solutions either focus on one type ofblindness only or lack the ability to estimate the degree of blindnessaccurately. Besides, heavy computation is needed so that these solutions cannotrun in real-time on practical systems. In this paper, we provide a method whichcould simultaneously detect the type of blindness and provide a blindness mapindicating to what degree the vision is limited on a pixel-by-pixel basis. Boththe blindness type and the estimate of per-pixel blindness are essential fortasks like deblur, dehaze, or the fail-safe functioning of robotic systems. Wedemonstrate the effectiveness of our approach on the KITTI and CUHK datasetswhere experiments show that our method outperforms other state-of-the-artapproaches, achieving speeds of about 130 frames per second (fps).

DeepBlindness: Fast Blindness Map Estimation and Blindness Type Classification for Outdoor Scene from Single Color Image