LUCE: Constrained Curve-Domain Guidance for Training-Free Low-Light Enhancement with Hue-Preserving Decoupling

Low-light image enhancement (LLIE) on pretrained diffu-sion models is commonly achieved through weighted pixel-space guidancelosses, whose competing gradients often destabilize sampling and inducecolor distortion. We present LUCE, a training-free framework that re-formulates sampler-side guidance as a constrained curve-domain lumi-nance control problem. Instead of directly optimizing pixel intensities,LUCE projects luminance guidance into a low-dimensional monotoniccurve space and enforces a unified luminance target via a single scalarenergy defined on a piecewise-linear LUT. This constrained parameteri-zation restricts guidance to a globally consistent exposure trajectory andremoves the need for balancing multiple heterogeneous losses. To preservechromatic fidelity, we introduce a hue-preserving chromatic decouplingmechanism that realizes luminance edits through per-pixel scalar gainswhile maintaining RGB direction. Implemented as a gradient-based cor-rection within a frozen DDPM sampler, LUCE requires no retraining orarchitectural modification. Experiments on paired and unpaired bench-marks demonstrate stable exposure adjustment and improved color fi-delity over prior training-free diffusion baselines.