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Learning-based fast image enhancement for driving scenes

Visual sensors such as cameras can obtain rich image and video information, which is one of the most effective and lowest cost perception sensors for autonomous driving. However, when the environmental conditions change rapidly, the quality of acquired images decreases, resulting in various deep learning algorithms embedded in the camera may be difficult to complete object detection and other tasks. Although some existing image enhancement methods can mitigate the effects of environmental factors, these methods often involve complex calculations that ignore the importance of processing speed in the autonomous driving field. To solve these problems, double-branch image enhancement network is proposed in this paper, which includes low resolution branch and full resolution branch. With low resolution branch, local features of image can be obtained at a small computational cost. With full resolution branch, the details which are lost due to down-sampling are obtained. The enhanced image is obtained by fusing the feature graphs of two branches. The test result on BDD100K shows that our method has higher image processing speed on the premise of ensuring image quality.