Learning Structural Illumination for Unsupervised Low-light Enhancement

• URL: http://arxiv.org/abs/2608.08153v1
• Date: Sat, 08 Aug 2026 14:27:56 GMT
• Title: Learning Structural Illumination for Unsupervised Low-light Enhancement
• Abstract summary: We propose aRelative Illumination Structure Estimation (RISE) framework that decouples relative illumination structure from absolute exposure.<n>For scene-adaptive exposure adjustment, we propose a Dual-Metering Exposure Reference derived from each input.<n>RISE achieves state-of-the-art performance among unsupervised LLIE methods while producing visually natural results.
• Score: 8.895269493077585
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing unsupervised low-light image enhancement (LLIE) methods often estimate illumination directly from the entire low-light input, without separating its spatially varying illumination pattern, termed relative illumination structure, from the absolute exposure level or preventing unreliable low signal-to-noise ratio regions from biasing the estimate. Moreover, fixed exposure targets impose a scene-agnostic enhancement criterion, limiting adaptation across diverse lighting conditions. Inspired by the spatial propagation of light, we propose a Relative Illumination Structure Estimation (RISE) framework that decouples relative illumination structure from absolute exposure and infers it from reliable bright regions, enabling interpretable and robust enhancement. For scene-adaptive exposure adjustment, we further propose a Dual-Metering Exposure Reference derived from each input, allowing RISE to adapt the enhancement strength to individual scenes and generalize across diverse lighting conditions. Extensive benchmark and real-world generalization experiments show that RISE achieves state-of-the-art performance among unsupervised LLIE methods while producing visually natural results.

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