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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 output produced by high-level semantic segmentation (SS)
network. However, if the output is not accurately estimated, it would affect
the high-level semantic feature (HSF) extraction, which accordingly interferes
with LLE. To this end, we develop a simple and effective semantic-aware LLE
network (SSLEN) composed of a LLE main-network (LLEmN) and a SS
auxiliary-network (SSaN). In SLLEN, LLEmN integrates the random intermediate
embedding feature (IEF), i.e., the information extracted from the intermediate
layer of SSaN, together with the HSF into a unified framework for better LLE.
SSaN is designed to act as a SS role to provide HSF and IEF. Moreover, thanks
to a shared encoder between LLEmN and SSaN, we further propose an alternating
training mechanism to facilitate the collaboration between them. Unlike
currently available approaches, the proposed SLLEN is able to fully lever the
semantic information, e.g., IEF, HSF, and SS dataset, to assist LLE, thereby
leading to a more promising enhancement performance. 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.
