构建物理驱动的镜头眩光数据集，提升去眩光模型真实场景泛化能力。
FlareX: A Physics-Informed Dataset for Lens Flare Removal via 2D Synthesis and 3D Rendering
- 基于光照物理规律分三阶段生成2D/3D混合眩光数据
- 含9500张2D模板与3000对3D渲染图像对
- 支持真实世界图像去眩光性能评估，适合视觉修复研究者
镜头眩光在拍摄强光源时会严重降低图像质量。由于真实世界中难以获取带眩光和无眩光的成对图像，现有数据集通常通过将人工模板叠加到背景图上进行二维合成。但模板多样性不足且合成过程忽略物理原理，导致模型在真实场景中泛化能力差。为此，我们提出一种新的物理感知眩光数据生成方法，包含三个阶段：参数化模板创建、光照感知的2D合成，以及基于物理引擎的3D渲染，最终形成融合2D与3D视角的混合数据集FlareX。该数据集包含9500张2D模板（来自95种眩光模式）和3000对由60个3D场景渲染的图像。此外，我们设计了一种掩码方法，从带眩光图像中恢复真实无眩光图像，以评估模型在真实图像上的表现。大量实验验证了方法与数据集的有效性。
原文摘要 · Abstract (English)
Lens flare occurs when shooting towards strong light sources, significantly degrading the visual quality of images. Due to the difficulty in capturing flare-corrupted and flare-free image pairs in the real world, existing datasets are typically synthesized in 2D by overlaying artificial flare templates onto background images. However, the lack of flare diversity in templates and the neglect of physical principles in the synthesis process hinder models trained on these datasets from generalizing well to real-world scenarios. To address these challenges, we propose a new physics-informed method for flare data generation, which consists of three stages: parameterized template creation, the laws of illumination-aware 2D synthesis, and physical engine-based 3D rendering, which finally gives us a mixed flare dataset that incorporates both 2D and 3D perspectives, namely FlareX. This dataset offers 9,500 2D templates derived from 95 flare patterns and 3,000 flare image pairs rendered from 60 3D scenes. Furthermore, we design a masking approach to obtain real-world flare-free images from their corrupted counterparts to measure the performance of the model on real-world images. Extensive experiments demonstrate the effectiveness of our method and dataset.
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