IRANet: Illumination-Aware Dual-Branch Reversible Aggregation Network for Low-Light Image Enhancement

Abstract

LLIE is challenged by illumination entanglement in the HVI color space, where coupling between illumination and chromaticity yields mismatched color–structure recovery. We propose the illumination-aware Dual-Branch Reversible Aggregation Network (IRANet), which reformulates enhancement as illumination-aware reasoning in HVI. However, under low-light conditions, the lack of long-distance contextual relationships compromises the stability of illumination-aware inference. To address the issue, we propose the Axis-Reversible Context Aggregator (ARCA), which restores long-distance context and stabilizes feature aggregation in both branches. The coherence of illumination reasoning is further hindered by limited synergy between the illumination and chromaticity branches. The Bidirectional illumination-aware Dependency (BIAD) mechanism establishes bidirectional illumination-aware dependencies between the I and HV branches, reinforcing coordinated fusion of illumination and chromaticity information. In addition, low-light enhancement still suffers from the lack of global consistency and local fidelity, for which the Multi-Scale Spatial–Channel Gating (MSCG) mechanism refines illumination and chromaticity information through spatial–channel gating and multi-scale channel gating to jointly maintain global consistency and local structural fidelity. Extensive experiments demonstrate that IRANet delivers SOTA performance with natural color restoration and structural fidelity under diverse low-light conditions.