Difficulty-Conditioned Attribute-Specific Restoration for Low-Light Image Enhancement

Low-light image enhancement (LLIE) aims to restore visi-bility and structural details from severely degraded images under insuf-ficient illumination. However, real-world low-light images often exhibitspatially heterogeneous degradations, where mildly under-exposed areasare easy to recover while severely dark, noisy, or color-unstable regionsremain challenging. Existing LLIE methods typically apply a globallyuniform enhancement strategy, which often leads to suboptimal trade-offs such as over-smoothing textures in hard regions or over-enhancementin easier areas. To address this, we propose a Difficulty-ConditionedAttribute-Specific Restoration (DCASR) framework that explicitly mod-els restoration difficulty and allocates enhancement capacity accordingly.DCASR incorporates a difficulty-aware enhancement network with twoprior-driven modulators: a Global Intensity Modulator (GIM) for co-herent illumination correction and a Structural Refinement Modulator(SRM) for detail recovery, together with an adaptive weighting schemethat emphasizes hard regions during optimization. Since residual-deriveddifficulty priors are unavailable at test time, we adopt a teacher–studentscheme in which a privileged teacher derives difficulty priors from resid-ual cues only during training, while a latent conditional diffusion studentgenerates aligned priors conditioned on a single low-light input at infer-ence. Experiments demonstrate that DCASR consistently outperformsstate-of-the-art methods on multiple benchmarks. Code will be madepublicly available at github.com/mingzhuzhang1/DCASR-LLIE.