Low-Light Image Enhancement via Adaptive Self-Reinforced Retinex Projection Model

Abstract

Low-light image enhancement is a longstanding challenge in computer vision, aiming to restore visibility and contrast in suboptimal lighting conditions. Recently, the illumination-based self-reinforced retinex projection (SRRP) model and its enhanced version (ESRRP) have made strides in eliminating artifact-inducing post-processing like gamma correction. However, this model often relies on the rigid and empirical exposure control parameter. This reliance restricts its adaptability, leading to under-enhancement in extremely dark scenes or over-exposure in moderately dim environments. This paper proposes an adaptive enhanced self-reinforced retinex projection (AESRRP) model to address the above limited adaptability in SRRP and ESRRP. In the proposed AESRRP model, we firstly introduce a novel adaptive mean-aware exposure mechanism that dynamically predicts a control parameter to replace the fixed empirical exposure control parameter using a non-linear sigmoid function with two saturation points, where the lower point ensures maximum brightening for dark inputs and the higher point prevents over-enhancement in brighter regions. Then, we develop a contrast-aware piecewise function incorporating illumination standard deviation and a fusion based spatially variant sub-image processing scheme with weighted aggregation to handle complex multi-exposure scenarios. Finally, we further develop a model that integrates the contrast-aware mechanism with fusion based spatially variant sub-image processing to enhance the performance of low-light image enhancement. Extensive experiments on standard benchmark LOL, LOL-v2 and SICE datasets demonstrate that the proposed AESRRP achieves a competitive performance with the state-of-the-arts methods like URetinex++ and outperforms the existing methods including modern neural networks like UTVNet, and its predecessor ESRRP in quantitative metrics.