  Most existing Low-light Image Enhancement (LLIE) methods either directly map
Low-Light (LL) to Normal-Light (NL) images or use semantic or illumination maps
as guides. However, the ill-posed nature of LLIE and the difficulty of semantic
retrieval from impaired inputs limit these methods, especially in extremely
low-light conditions. To address this issue, we present a new LLIE network via
Generative LAtent feature based codebook REtrieval (GLARE), in which the
codebook prior is derived from undegraded NL images using a Vector Quantization
(VQ) strategy. More importantly, we develop a generative Invertible Latent
Normalizing Flow (I-LNF) module to align the LL feature distribution to NL
latent representations, guaranteeing the correct code retrieval in the
codebook. In addition, a novel Adaptive Feature Transformation (AFT) module,
featuring an adjustable function for users and comprising an Adaptive Mix-up
Block (AMB) along with a dual-decoder architecture, is devised to further
enhance fidelity while preserving the realistic details provided by codebook
prior. Extensive experiments confirm the superior performance of GLARE on
various benchmark datasets and real-world data. Its effectiveness as a
preprocessing tool in low-light object detection tasks further validates GLARE
for high-level vision applications. Code is released at
https://github.com/LowLevelAI/GLARE.
