用最优传输理论提升复杂光照下光源方向估计精度
Illuminant and light direction estimation using Wasserstein distance method
- 基于沃尔德斯坦距离的最优传输方法，捕捉光照分布差异
- 在室内外、黑白及夜间图像中均优于传统统计方法
- 适合机器人环境感知与图像质量评估场景
光照估计仍是图像处理中的关键挑战，尤其在机器人领域，需在多变光照下实现鲁棒环境感知。传统方法如RGB直方图和GIST描述符在复杂场景中因对光照变化敏感而失效。本文提出一种基于最优传输理论的沃尔德斯坦距离新方法，用于估计图像中的光源颜色（illuminant）和光照方向。在多种室内场景、黑白照片及夜间图像上的实验表明，该方法能有效检测主导光源并估计其方向，在复杂光照环境下显著优于传统统计方法。研究结果对光源定位、图像质量评估和目标检测增强具有应用潜力。未来可探索自适应阈值与梯度分析结合，进一步提升精度，为机器人等实际场景提供可扩展的光照应对方案。
原文摘要 · Abstract (English)
Illumination estimation remains a pivotal challenge in image processing, particularly for robotics, where robust environmental perception is essential under varying lighting conditions. Traditional approaches, such as RGB histograms and GIST descriptors, often fail in complex scenarios due to their sensitivity to illumination changes. This study introduces a novel method utilizing the Wasserstein distance, rooted in optimal transport theory, to estimate illuminant and light direction in images. Experiments on diverse images indoor scenes, black-and-white photographs, and night images demonstrate the method's efficacy in detecting dominant light sources and estimating their directions, outperforming traditional statistical methods in complex lighting environments. The approach shows promise for applications in light source localization, image quality assessment, and object detection enhancement. Future research may explore adaptive thresholding and integrate gradient analysis to enhance accuracy, offering a scalable solution for real-world illumination challenges in robotics and beyond.
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