Learning Structural Illumination for Unsupervised Low-light Enhancement
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 f...
| Authors | Tianle Du, Peiyuan He, Hainuo Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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