通过多尺度光照估计融合，提升复杂光照下图像色彩一致性。
Multi-illuminant Color Constancy via Multi-scale Illuminant Estimation and Fusion
- 用多尺度图像提取不同粒度的光照分布图
- 自适应注意力融合模块实现最优光照估计
- 在多个数据集上达到当前最佳效果
多光照色彩恒常性方法旨在通过逐像素估计光照来消除图像中的局部色偏。现有方法主要依赖深度学习建立图像与光照图之间的直接映射，忽视了图像尺度的影响。为此，本文将光照图表示为多尺度图像估计出的各分量的线性组合，并提出一种三分支卷积网络，从多尺度图像中估计多粒度光照分布图。这些多粒度光照图通过注意力机制的光照融合模块进行自适应融合。通过全面的实验分析与评估，结果表明该方法有效，在多个基准上达到了当前最优性能。频率分析显示，光照图可能包含高频成分，违反了平滑性假设，且随着尺度减小，其频谱向低频区域转移。
原文摘要 · Abstract (English)
Multi-illuminant color constancy methods aim to eliminate local color casts within an image through pixel-wise illuminant estimation. Existing methods mainly employ deep learning to establish a direct mapping between an image and its illumination map, which neglects the impact of image scales. To alleviate this problem, we represent an illuminant map as the linear combination of components estimated from multi-scale images. Furthermore, we propose a tri-branch convolution networks to estimate multi-grained illuminant distribution maps from multi-scale images. These multi-grained illuminant maps are merged adaptively with an attentional illuminant fusion module. Through comprehensive experimental analysis and evaluation, the results demonstrate the effectiveness of our method, and it has achieved state-of-the-art performance.
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