  Low-light image enhancement is a promising solution to tackle the problem of
insufficient sensitivity of human vision system (HVS) to perceive information
in low light environments. Previous Retinex-based works always accomplish
enhancement task by estimating light intensity. Unfortunately, single light
intensity modelling is hard to accurately simulate visual perception
information, leading to the problems of imbalanced visual photosensitivity and
weak adaptivity. To solve these problems, we explore the precise relationship
between light source and visual perception and then propose the visual
perception (VP) model to acquire a precise mathematical description of visual
perception. The core of VP model is to decompose the light source into light
intensity and light spatial distribution to describe the perception process of
HVS, offering refinement estimation of illumination and reflectance. To reduce
complexity of the estimation process, we introduce the rapid and adaptive
$\mathbf{\beta}$ and $\mathbf{\gamma}$ functions to build an illumination and
reflectance estimation scheme. Finally, we present a optimal determination
strategy, consisting of a \emph{cycle operation} and a \emph{comparator}.
Specifically, the \emph{comparator} is responsible for determining the optimal
enhancement results from multiple enhanced results through implementing the
\emph{cycle operation}. By coordinating the proposed VP model, illumination and
reflectance estimation scheme, and the optimal determination strategy, we
propose a rapid and adaptive framework for low-light image enhancement.
Extensive experiment results demenstrate that the proposed method achieves
better performance in terms of visual comparison, quantitative assessment, and
computational efficiency, compared with the currently state-of-the-arts.
