Abstract
Single LDR to HDR reconstruction remains challenging for over-exposed regions where traditional methods often fail due to complete information loss. We present a training-free approach that enhances existing indirect and direct HDR reconstruction methods through diffusion-based inpainting. Our method combines text-guided diffusion models with SDEdit refinement to generate plausible content in over-exposed areas while maintaining consistency across multi-exposure LDR images. Unlike previous approaches requiring extensive training, our method seamlessly integrates with existing HDR reconstruction techniques through an iterative compensation mechanism that ensures luminance coherence across multiple exposures. We demonstrate significant improvements in both perceptual quality and quantitative metrics on standard HDR datasets and in-the-wild captures. Results show that our method effectively recovers natural details in challenging scenarios while preserving the advantages of existing HDR reconstruction pipelines. Project page: this https URL
Abstract (translated)
单次LDR到HDR的重建对于过度曝光区域而言仍然具有挑战性，因为传统方法往往由于信息完全丢失而失败。我们提出了一种无需训练的方法，通过基于扩散的修复技术增强现有的间接和直接HDR重建方法。我们的方法结合了文本引导的扩散模型与SDEdit细化技术，在保持多曝光LDR图像之间一致性的同时，生成过度曝光区域中的合理内容。不同于以往需要大量训练的方法，我们的方法通过迭代补偿机制无缝地整合到现有HDR重建技术中，并确保不同曝光之间的亮度连贯性。 我们在标准HDR数据集和现实场景拍摄的照片上展示了感知质量和量化指标的显著改进。结果显示，该方法在具有挑战性的场景下有效恢复自然细节的同时，还保留了现有HDR重建流程的优势。项目页面：[请参阅链接以获取更多信息](https://this https URL)
URL
https://arxiv.org/abs/2602.19706
