AI RESEARCH
Learning Reference-Guided Exposure Correction with Hybrid Illumination Characteristics
arXiv CS.CV
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ArXi:2605.26729v1 Announce Type: new We present HICNet, a reference-guided exposure correction framework. A lightweight, content-agnostic encoder distills each image into a compact illumination embedding capturing regional brightness, edge contrast, and higher-order luminance moments. The embedding difference between a source and its reference drives a multi-scale modulation network that combines FiLM-based global adjustment with Photometric Channel Rebalancing for fine-grained, illumination-aware spectral gating, producing exposure-matched outputs while faithfully preserving scene details.