  Enhancing low-light images while maintaining natural colors is a challenging
problem due to camera processing variations and limited access to photos with
ground-truth lighting conditions. The latter is a crucial factor for supervised
methods that achieve good results on paired datasets but do not handle
out-of-domain data well. On the other hand, unsupervised methods, while able to
generalize, often yield lower-quality enhancements. To fill this gap, we
propose Dimma, a semi-supervised approach that aligns with any camera by
utilizing a small set of image pairs to replicate scenes captured under extreme
lighting conditions taken by that specific camera. We achieve that by
introducing a convolutional mixture density network that generates distorted
colors of the scene based on the illumination differences. Additionally, our
approach enables accurate grading of the dimming factor, which provides a wide
range of control and flexibility in adjusting the brightness levels during the
low-light image enhancement process. To further improve the quality of our
results, we introduce an architecture based on a conditional UNet. The
lightness value provided by the user serves as the conditional input to
generate images with the desired lightness. Our approach using only few image
pairs achieves competitive results compared to fully supervised methods.
Moreover, when trained on the full dataset, our model surpasses
state-of-the-art methods in some metrics and closely approaches them in others.
