Beyond Color Remapping: Photometric–Detail Network for Low-Light Image Enhancement

Lookup table (LUT)-based enhancement provides an explicit dark-to-bright mapping through indexing and interpolation. However, existing paradigms remain constrained by two key limitations. RGB-only indices cannot distinguish pixels with similar chromaticity but different illumination and contrast conditions, whereas channel-independent spatial lookup neglects cross-channel structural coherence. To address these limitations, we propose the Photometric-Detail Network (PD-Net), a two-stage framework that explicitly models photometric degradation and spatial detail separately. First, we develop a Dual-Condition Photometric LUT (DCP-LUT) that integrates RGB values, an illumination map, and a dual-scale contrast map into a 5D degradation-aware index. This index enables joint correction of brightness, color, and contrast. Second, we introduce a Channel-Pair Spatial Detail LUT (CSD-LUT) that improves texture consistency through channel-pair projection, rotation-cascaded dilated L-shaped lookup, and cyclic Gauss-Seidel propagation. Furthermore, we factorize each 5D LUT into a sum of rank-one tensors using canonical polyadic (CP) decomposition, reducing storage complexity from $O(N^5)$ to $O(KN)$ without explicitly materializing dense grids. Evaluations on nine benchmarks under full-reference and no-reference protocols show that PD-Net achieves state-of-the-art performance while retaining a compact memory footprint and explicit lookup-based inference.