Rethinking probabilistic learning for counterfactual low-light image enhancement in robust engineering vision systems

• 1: Formulates low-light enhancement as a counterfactual intervention on a Retinex SCM. • 2: Introduces a physics-guided structural causal model for illumination–reflectance reasoning. • 3: Achieves superior perceptual fidelity and robustness on multiple benchmark datasets. Many Architecture, Engineering, and Construction (AEC) operations must operate safely under visually compromised conditions, dimensional dimension, including night-time navigation/driving, tunnel or cave exploration, and hazardous or confined sites observation. In such conditions, low-light image enhancement is expected to be more than simply image brightening but also to maintain mission-critical structures and be visually interpretable for deployment in safety-critical conditions. Nonetheless, current deep LIE techniques conceptually overlook image processing, physical priors and causal mechanisms during improvement, contributing to constrained ruggedness and generalisability. This paper presents a Learning Probabilistic Low-light Image Enhancement (LPIE) network that explicitly integrates causal factors into the LPIE and allows for probabilistic counterfactual reasoning. LPIE casts illumination enhancement in the light of an intervention and unrolls it into a structured causal model of image formation. A normalizing-flow module performs invertible and physically coherent illumination processes, while an uncertainty-aware Transformer recovers reflectance in spatially varied and high-textured regions. Consequently, a complete probabilistic refinement further optimizes all elements to optimize the distribution of physical variables, resulting in a natural, fine-detailed photo and supporting across-used scenarios. Experiments on public benchmarks and AEC-relevant datasets show that LPIE achieves state-of-the-art performance and clearly surpasses recent LIE methods on the widely used benchmark dataset, paving the way for more interpretable, robust and trustworthy automated perception systems.