  Image frames obtained in darkness are special. Just multiplying by a constant
doesn't restore the image. Shot noise, quantization effects and camera
non-linearities mean that colors and relative light levels are estimated
poorly. Current methods learn a mapping using real dark-bright image pairs.
These are very hard to capture. A recent paper has shown that simulated data
pairs produce real improvements in restoration, likely because huge volumes of
simulated data are easy to obtain. In this paper, we show that respecting
equivariance -- the color of a restored pixel should be the same, however the
image is cropped -- produces real improvements over the state of the art for
restoration. We show that a scale selection mechanism can be used to improve
reconstructions. Finally, we show that our approach produces improvements on
video restoration as well. Our methods are evaluated both quantitatively and
qualitatively.
