Fractal pyramid low-light image enhancement network with illumination information

Low-light images suffer from many problems, including low contrast, low brightness, color distortion, blurred details, and noise, which adversely affect the performance of many advanced computer vision tasks. There have been a variety of deep-learning-based methods used to enhance low-light images in recent years. These methods, however, fail to calculate the illumination information and neglect the relationship between multi-scale features and contextual information, which lead to not only poor model generalization but also poor color and details enhancement. To address these concerns, we propose a two-stage low-light image enhancement network called the fractal pyramid network with illumination information (FPN-IL). On the one hand, we use a code network added spatial channel attention mechanism to extract the lighting information in case of uneven exposure and overexposure. On the other hand, we combine the fractal and pyramid networks to construct a new coding method. By having multiple processing paths for information, the FPN-IL is able to make full use of contextual information and interactions of features at different scales. Thus, the image's details could be abundant. The results demonstrate the advantages of our method compared with other methods, from both qualitative and quantitative perspectives.