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至关重要。现有方法通常仅利用从高级语义分割网络输出中提取的语义特征。然而，若输出估计不准确，将影响高级语义特征的提取，进而干扰低光照图像增强。为此，我们提出了一种简单有效的语义感知低光照图像增强网络，该网络由低光照图像增强主网络和语义分割辅助网络构成。在SLLEN中，低光照图像增强主网络将随机中间嵌入特征（即从语义分割辅助网络中间层提取的信息）与高级语义特征整合到统一框架中，以实现更优的低光照图像增强。语义分割辅助网络被设计为提供高级语义特征和中间嵌入特征的语义分割模块。此外，得益于低光照图像增强主网络与语义分割辅助网络之间的共享编码器，我们进一步提出了一种交替训练机制以促进两者间的协作。与现有方法不同，所提出的SLLEN能够充分利用语义信息（如中间嵌入特征、高级语义特征及语义分割数据集）来辅助低光照图像增强，从而获得更优越的增强性能。所提SLLEN与其他先进技术的对比实验表明，SLLEN在低光照图像增强质量方面优于所有可比方法。