Teaching Lookup Tables to See in the Dark: Learning Complete Mappings from a Diffusion Teacher

Low-light image enhancement (LLIE) aims to restore illumination, color, and structural edges in images captured under poor lighting. Existing LUT-based methods construct image mappings by combining basis LUTs, but are constrained by predefined transformations and supervised primarily by queries induced by the input image, leaving many grid points in high-dimensional LUTs weakly constrained. To address this limitation, this work proposes a diffusion-teacher-guided LUT distillation method for learning complete image-adaptive mappings. In the wavelet domain, the low-frequency LUT corrects brightness, contrast, and color, while the high-frequency residual LUT restores structures and suppresses noise. Specifically, target LUTs are first obtained from paired images through constrained inversion, after which a conditional diffusion teacher learns residual corrections from coarse LUTs to the target LUTs. Teacher and student interpolation outputs are then aligned using both image queries and LUT-grid queries, extending supervision to sparsely accessed regions of the LUT domain. At inference, the student predicts complete LUTs without diffusion sampling; CP decomposition provides a compact representation for lookup. Experiments on multiple benchmarks demonstrate state-of-the-art performance across a range of evaluation metrics.