Woo Sung-Min, Kim Jong-Ok
IEEE Trans Image Process. 2021;30:3623-3636. doi: 10.1109/TIP.2021.3062729.
A new dichromatic illuminant estimation method using a deep neural network is proposed. Previous methods based on the dichromatic reflection model commonly suffer from inaccurate separation of specularity, thus being limited in their use in a real-world. Recent deep neural network-based methods have shown a significant improvement in the estimation of the illuminant color. However, why they succeed or fail is not explainable easily, because most of them estimate the illuminant color at the network output directly. To tackle these problems, the proposed architecture is designed to learn dichromatic planes and their confidences using a deep neural network with novel losses function. The illuminant color is estimated by a weighted least mean square of these planes. The proposed dichromatic guided learning not only achieves compelling results among state-of-the-art color constancy methods in standard real-world benchmark evaluations, but also provides a map to include color and regional contributions for illuminant estimation, which allow for an in-depth analysis of success and failure cases of illuminant estimation.
提出了一种使用深度神经网络的新型双色光源估计方法。以往基于双色反射模型的方法通常存在镜面反射分离不准确的问题，因此在实际应用中受到限制。最近基于深度神经网络的方法在光源颜色估计方面有了显著改进。然而，它们成功或失败的原因并不容易解释，因为大多数方法直接在网络输出端估计光源颜色。为了解决这些问题，所提出的架构旨在使用具有新颖损失函数的深度神经网络来学习双色平面及其置信度。通过这些平面的加权最小均方来估计光源颜色。所提出的双色引导学习不仅在标准真实世界基准评估中在现有最先进的颜色恒常性方法中取得了令人信服的结果，还提供了一个包含颜色和区域对光源估计贡献的映射图，这有助于深入分析光源估计的成功和失败案例。
