Qiu Jueqin, Xu Haisong, Ye Zhengnan
IEEE Trans Image Process. 2020 Apr 13. doi: 10.1109/TIP.2020.2985296.
In this study, a novel illuminant color estimation framework is proposed for computational color constancy, which incorporates the high representational capacity of deep-learningbased models and the great interpretability of assumptionbased models. The well-designed building block, feature map reweight unit (ReWU), helps to achieve comparative accuracy on benchmark datasets with respect to prior state-of-the-art deep learning based models while requiring more compact model size and cheaper computational cost. In addition to local color estimation, a confidence estimation branch is also included such that the model is able to simultaneously produce point estimate and its uncertainty estimate, which provides useful clues for local estimates aggregation and multiple illumination estimation. The source code and the dataset have been made available1.
在本研究中，针对计算颜色恒常性提出了一种新颖的光源颜色估计框架，该框架融合了基于深度学习模型的高表征能力和基于假设模型的强解释性。精心设计的构建模块，即特征图重加权单元（ReWU），有助于在基准数据集上相对于先前基于深度学习的先进模型实现相当的精度，同时需要更紧凑的模型大小和更低的计算成本。除了局部颜色估计外，还包括一个置信度估计分支，使得该模型能够同时产生点估计及其不确定性估计，这为局部估计聚合和多光源估计提供了有用的线索。源代码和数据集已公开。
