  Raw low light image enhancement (LLIE) has achieved much better performance
than the sRGB domain enhancement methods due to the merits of raw data.
However, the ambiguity between noisy to clean and raw to sRGB mappings may
mislead the single-stage enhancement networks. The two-stage networks avoid
ambiguity by decoupling the two mappings but usually have large computing
complexity. To solve this problem, we propose a single-stage network empowered
by Feature Domain Adaptation (FDA) to decouple the denoising and color mapping
tasks in raw LLIE. The denoising encoder is supervised by the clean raw image,
and then the denoised features are adapted for the color mapping task by an FDA
module. We propose a Lineformer to serve as the FDA, which can well explore the
global and local correlations with fewer line buffers (friendly to the
line-based imaging process). During inference, the raw supervision branch is
removed. In this way, our network combines the advantage of a two-stage
enhancement process with the efficiency of single-stage inference. Experiments
on four benchmark datasets demonstrate that our method achieves
state-of-the-art performance with fewer computing costs (60% FLOPs of the
two-stage method DNF). Our codes will be released after the acceptance of this
work.
