Rethinking Artifact Mitigation in HDR Reconstruction: From Detection to Optimization

Artifact remains a long-standing challenge in High Dynamic Range (HDR) reconstruction. Existing methods focus on model designs for artifact mitigation but ignore explicit detection and suppression strategies. Because artifact lacks clear boundaries, distinct shapes, and semantic consistency, and there is no existing dedicated dataset for HDR artifact, progress in direct artifact detection and recovery is impeded. To bridge the gap, we propose a unified HDR reconstruction framework that integrates artifact detection and model optimization. Firstly, we build the first HDR artifact dataset (HADataset), comprising 1,213 diverse multi-exposure Low Dynamic Range (LDR) image sets and 1,765 HDR image pairs with per-pixel artifact annotations. Secondly, we develop an effective HDR artifact detector (HADetector), a robust artifact detection model capable of accurately localizing HDR reconstruction artifact. HADetector plays two pivotal roles: (1) enhancing existing HDR reconstruction models through fine-tuning, and (2) serving as a non-reference image quality assessment (NR-IQA) metric, the Artifact Score (AS), which aligns closely with human visual perception for reliable quality evaluation. Extensive experiments validate the effectiveness and generalizability of our framework, including the HADataset, HADetector, fine-tuning paradigm, and AS metric.