LLFA: Fusing Global Illumination and Local Priors for Low-Light Face Image Enhancement with Adaptor

Abstract

Low-light image enhancement problem has been widely studied. However, most existing methods do not perform well on low-light face images due to no specific facial characteristic considerations. We first create large-scale low-light face datasets with synthesized and real-world images to address the absence of suitable datasets. Our experiments show that existing LLIE and face restoration methods are limited in enhancing low-light face images. To overcome these challenges, we propose a novel framework, the Low-Light Face Adaptor (LLFA), featuring an auxiliary encoder and an adaptor module. The encoder captures global illumination information, while the adaptor module adaptively fuses this information with high-quality priors. We also introduce a joint learning strategy that optimizes the model by simultaneously learning face priors and the enhancement process. Comprehensive experiments demonstrate that LLFA significantly outperforms state-of-the-art methods.