  In this paper, we study the problem of making brighter images from dark
images found in the wild. The images are dark because they are taken in dim
environments. They suffer from color shifts caused by quantization and from
sensor noise. We don't know the true camera reponse function for such images
and they are not RAW. We use a supervised learning method, relying on a
straightforward simulation of an imaging pipeline to generate usable dataset
for training and testing. On a number of standard datasets, our approach
outperforms the state of the art quantitatively. Qualitative comparisons
suggest strong improvements in reconstruction accuracy.
