用新数据集和变换器模型提升多光源白平衡融合效果
Revisiting Image Fusion for Multi-Illuminant White-Balance Correction
- 用变换器捕捉不同白平衡设置间的空间关系，替代线性融合
- 在超1.6万张多光源图像上测试，性能提升达100%
- 构建了首个大规模多光源白平衡数据集，支持训练与评估
多光源场景下的白平衡校正仍是计算机视觉的难题。现有融合方法通过神经网络线性混合输入图像在多个预设白平衡设置下的sRGB版本，但我们在常见多光源场景中发现其表现不佳。此外，现有方法依赖缺乏专门多光源图像的sRGB白平衡数据集，限制了训练与评估。为此，我们提出两项关键贡献：第一，设计一种高效的基于变换器的模型，能有效捕捉sRGB白平衡设置间的空间依赖，显著优于线性融合；第二，构建一个大规模多光源数据集，包含超过16,000张使用五种不同白平衡设置渲染的sRGB图像及对应的白平衡校正图像。我们的方法在新数据集上性能相比现有技术最高提升100%。
原文摘要 · Abstract (English)
White balance (WB) correction in scenes with multiple illuminants remains a persistent challenge in computer vision. Recent methods explored fusion-based approaches, where a neural network linearly blends multiple sRGB versions of an input image, each processed with predefined WB presets. However, we demonstrate that these methods are suboptimal for common multi-illuminant scenarios. Additionally, existing fusion-based methods rely on sRGB WB datasets lacking dedicated multi-illuminant images, limiting both training and evaluation. To address these challenges, we introduce two key contributions. First, we propose an efficient transformer-based model that effectively captures spatial dependencies across sRGB WB presets, substantially improving upon linear fusion techniques. Second, we introduce a large-scale multi-illuminant dataset comprising over 16,000 sRGB images rendered with five different WB settings, along with WB-corrected images. Our method achieves up to 100\% improvement over existing techniques on our new multi-illuminant image fusion dataset.
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