基于平滑技术的多光源场景像素级颜色恒常性
Pixel-Wise Color Constancy via Smoothness Techniques in Multi-Illuminant Scenes
AI总结：
针对多光源场景颜色恒常性问题，提出通过学习像素级照明图并利用总变差损失、双边滤波和标签平滑技术，实现优于现有方法的性能。
摘要：
大多数场景由多个光源照明，传统均匀照明的假设在此情况下不成立。多数颜色恒常性方法忽略了这一问题，主要原因是多个光源对图像的空间影响复杂。此外，现有的大多数多光源方法未能保持照明的平滑变化，这源于自然图像中的空间依赖性。受此启发，我们提出了一种新颖的多光源颜色恒常性方法，通过学习由多个光源引起的像素级照明图。所提方法通过使用总变差损失对训练进行正则化，强制相邻像素之间的平滑性。此外，提供双边滤波器以进一步增强估计图像的自然外观，同时保留边缘。另外，我们提出了一种标签平滑技术，使模型在真实标签存在不确定性的情况下仍能良好泛化。定量和定性实验表明，所提方法优于现有最先进方法。
英文摘要：
Most scenes are illuminated by several light sources, where the traditional assumption of uniform illumination is invalid. This issue is ignored in most color constancy methods, primarily due to the complex spatial impact of multiple light sources on the image. Moreover, most existing multi-illuminant methods fail to preserve the smooth change of illumination, which stems from spatial dependencies in natural images. Motivated by this, we propose a novel multi-illuminant color constancy method, by learning pixel-wise illumination maps caused by multiple light sources. The proposed method enforces smoothness within neighboring pixels, by regularizing the training with the total variation loss. Moreover, a bilateral filter is provisioned further to enhance the natural appearance of the estimated images, while preserving the edges. Additionally, we propose a label-smoothing technique that enables the model to generalize well despite the uncertainties in ground truth. Quantitative and qualitative experiments demonstrate that the proposed method outperforms the state-of-the-art.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
