Low-light Image Enhancement Based on Multi-exposure Images Generation

Low-light images will cause the robustness degradation in many computer vision algorithms, which seriously affects various vision tasks in the context of robotics, such as automatic driving, image recognition and target tracking. In order to obtain the enhanced image with more details and a larger dynamic range, a low-light image enhancement method based on multi-exposure images generation is proposed. By analyzing the real-captured multi-exposure images, it is found that there is a linear relationship between pixels of the images with different exposure time, so the idea of orthogonal decomposition can be applied to generating multi-exposure images. Because the multi-exposure images are generated according to the physical imaging mechanism, they are similar to the real-captured images. After the original image is decomposed into an illumination invariant component and an illumination component, an adaptive algorithm is designed to generate various illumination components, then the multi-exposure images are generated by combining the various illumination components with the invariant one. Finally, a multi-exposure image fusion strategy is applied to obtaining the enhanced image with a larger dynamic range. The fusion result is consistent with the input images, and the final enhanced image can effectively retrain the colour of the original image with high naturalness. The proposed method is compared with the existing advanced algorithms through experiments on the public dataset of real-captured low-light images, and the results show that the structural similarity between the enhanced image and the reference image is improved by 2.1% by this method, the feature similarity is improved by 4.6%, and the image enhanced by this method is closer to the reference image and more natural than the others.