A Novel Low-light Image Enhancement Algorithm Based On Information Assistance

Low-light image enhancement plays a significant role in image processing and analysis. Nevertheless, most current algorithms are suffering from the problem of color distortion. With the assistance of an image captured in normal illumination as a reference, the matter of color distortion can be effectively settled by means of the color transfer method. The enhanced images in our proposed algorithm are much closer to the color distribution of the original objects under normal illumination. Our proposed algorithm takes a low-light image and a reference image with a different viewpoint or similar scene as input. Two images are both divided into regions by FCM (Fuzzy C-means). Accordingly, they are then transferred into HSV color space, and the optimal matched results would be obtained by minimizing the weighted distance of the H and S channels. Eventually, the original image would be significantly enhanced using our improved color transfer algorithm. Therefore, this novel low-light image enhancement algorithm based on information assistance expands the application of color transfer in the field of low-light image enhancement. In conclusion, the proposed algorithm has made superior achievements in image contrast retention, color consistency, and naturalness preservation, which would be promising in broad application scenarios.