  Existing methods for enhancing dark images captured in a very low-light
environment assume that the intensity level of the optimal output image is
known and already included in the training set. However, this assumption often
does not hold, leading to output images that contain visual imperfections such
as dark regions or low contrast. To facilitate the training and evaluation of
adaptive models that can overcome this limitation, we have created a dataset of
1500 raw images taken in both indoor and outdoor low-light conditions. Based on
our dataset, we introduce a deep learning model capable of enhancing input
images with a wide range of intensity levels at runtime, including ones that
are not seen during training. Our experimental results demonstrate that our
proposed dataset combined with our model can consistently and effectively
enhance images across a wide range of diverse and challenging scenarios.
