  In this paper, we make the first benchmark effort to elaborate on the
superiority of using RAW images in the low light enhancement and develop a
novel alternative route to utilize RAW images in a more flexible and practical
way. Inspired by a full consideration on the typical image processing pipeline,
we are inspired to develop a new evaluation framework, Factorized Enhancement
Model (FEM), which decomposes the properties of RAW images into measurable
factors and provides a tool for exploring how properties of RAW images affect
the enhancement performance empirically. The empirical benchmark results show
that the Linearity of data and Exposure Time recorded in meta-data play the
most critical role, which brings distinct performance gains in various measures
over the approaches taking the sRGB images as input. With the insights obtained
from the benchmark results in mind, a RAW-guiding Exposure Enhancement Network
(REENet) is developed, which makes trade-offs between the advantages and
inaccessibility of RAW images in real applications in a way of using RAW images
only in the training phase. REENet projects sRGB images into linear RAW domains
to apply constraints with corresponding RAW images to reduce the difficulty of
modeling training. After that, in the testing phase, our REENet does not rely
on RAW images. Experimental results demonstrate not only the superiority of
REENet to state-of-the-art sRGB-based methods and but also the effectiveness of
the RAW guidance and all components.
