AI RESEARCH

Rethinking Exposure Correction for Spatially Non-uniform Degradation

arXiv CS.CV

ArXi:2604.04136v1 Announce Type: new Real-world exposure correction is fundamentally challenged by spatially non-uniform degradations, where diverse exposure errors frequently coexist within a single image. However, existing exposure correction methods are still largely developed under a predominantly uniform assumption. Architecturally, they typically rely on globally aggregated modulation signals that capture only the overall exposure trend.