Visual Perception Model for Rapid and Adaptive Low-light Image Enhancement
This work addresses the challenge of enhancing images in low-light conditions for applications like photography and surveillance, though it appears incremental as it builds on existing Retinex-based approaches.
The paper tackled the problem of low-light image enhancement by proposing a visual perception model that decomposes light into intensity and spatial distribution, resulting in improved performance in visual quality, quantitative metrics, and computational efficiency compared to state-of-the-art methods.
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 $\mathbfβ$ and $\mathbfγ$ 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.