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

Dimma: Semi-supervised Low Light Image Enhancement with Adaptive Dimming