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We presented a method for improving computer vision tasks on images affectedby adverse weather conditions, including distortions caused by adherentraindrops. Overcoming the challenge of applying computer vision to imagesaffected by adverse weather conditions is essential for autonomous vehiclesutilizing RGB cameras. For this purpose, we trained an appropriate generativeadversarial network and showed that it was effective at removing the effect ofthe distortions, in the context of image reconstruction and computer visiontasks. We showed that object recognition, a vital task for autonomous drivingvehicles, is completely impaired by the distortions and occlusions caused byadherent raindrops and that performance can be restored by our de-rainingmodel. The approach described in this paper could be applied to all adverseweather conditions.

Generating Clear Images From Images With Distortions Caused by Adverse Weather Using Generative Adversarial Networks