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SLLEN: Semantic-aware Low-light Image Enhancement Network

by   Mingye Ju, et al.
Nanjing University
Nanjing University of Posts and Telecommunications
The University of Sydney

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.


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