How to effectively explore semantic feature is vital for low-light image
enhancement (LLE). Existing methods usually utilize the semantic feature that
is only drawn from the semantic map produced by high-level semantic
segmentation network (SSN). However, if the semantic map is not accurately
estimated, it would affect the high-level semantic feature (HSF) extraction,
which accordingly interferes with LLE. In this paper, we develop a simple yet
effective two-branch semantic-aware LLE network (SLLEN) that neatly integrates
the random intermediate embedding feature (IEF) (i.e., the information
extracted from the intermediate layer of semantic segmentation network)
together with the HSF into a unified framework for better LLE. Specifically,
for one branch, we utilize an attention mechanism to integrate HSF into
low-level feature. For the other branch, we extract IEF to guide the adjustment
of low-level feature using nonlinear transformation manner. Finally,
semantic-aware features obtained from two branches are fused and decoded for
image enhancement. It is worth mentioning that IEF has some randomness compared
to HSF despite their similarity on semantic characteristics, thus its
introduction can allow network to learn more possibilities by leveraging the
latent relationships between the low-level feature and semantic feature, just
like the famous saying "God rolls the dice" in Physics Nobel Prize 2022.
Comparisons between the proposed SLLEN and other state-of-the-art techniques
demonstrate the superiority of SLLEN with respect to LLE quality over all the
comparable alternatives