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.

翻译：如何有效探索语义特征对于低光照图像增强（LLE）至关重要。现有方法通常仅利用由高层语义分割（SS）网络输出所提取的语义特征。然而，若该输出估计不准确，则会直接影响高层语义特征（HSF）的提取，进而干扰LLE效果。为此，我们提出一种简单且有效的语义感知低光照图像增强网络（SLLEN），由LLE主网络（LLEmN）和SS辅助网络（SSaN）组成。在SLLEN中，LLEmN将随机中间嵌入特征（IEF）（即从SSaN中间层提取的信息）与HSF整合至统一框架中，以实现更优的LLE效果。SSaN被设计为承担SS角色，以提供HSF和IEF。此外，得益于LLEmN与SSaN间的共享编码器，我们进一步提出交替训练机制以促进两者协作。与现有方法不同，所提出的SLLEN能够充分运用语义信息（如IEF、HSF及SS数据集）辅助LLE，从而获得更具竞争力的增强性能。将所提SLLEN与其他前沿技术的对比表明，在所有可比较的替代方案中，SLLEN在LLE质量上均展现出显著优势。