目的 针对当前自动白平衡（AWB）方法在跨传感器应用中存在的泛化性差、数据采集成本高等问题,提出一种无需目标设备图像的跨设备AWB策略。方法 构建融合动态色温校准与语义特征建模的方法。在校正阶段,选取多个典型色温下的白点构建分段对角映射矩阵,用于将训练图像重构至目标传感器响应域,实现数据分布对齐;随后,在训练阶段引入语义特征提取模块,以增强模型对跨设备光源估计的鲁棒性。结果 实验在多个典型AWB数据集中均取得了领先的估计精度,具备良好的泛化能力和稳定性。结论 所提方法实现了无需目标图像参与的跨设备AWB估计,大幅降低了数据采集成本,并显著提升了模型的部署效率。
Abstract
To solve the problems of poor generalization and high data acquisition costs in cross-sensor applications of the current Auto White Balance (AWB) method, the work aims to propose a cross-device AWB strategy that requires no images from the target device. A method combining dynamic calibration at multiple typical color temperatures with semantic feature modeling was established. During the calibration, white points at multiple typical color temperatures were selected to construct a piecewise diagonal mapping matrix. This matrix was used to reconstruct training images into the target sensor's response domain, achieving data distribution alignment. Subsequently, in the training phase, a semantic feature extraction module was introduced to enhance the model's robustness for cross-device illuminant estimation. Experiments achieved excellent estimation accuracy across multiple standard AWB datasets, demonstrating strong generalization capability and stability. The proposed method enables cross-device AWB estimation without requiring any target device images, substantially reducing data acquisition costs and significantly improving model deployment efficiency.
关键词
白平衡 /
颜色恒常性 /
颜色校正 /
域适应
Key words
white balance /
color constancy /
color calibration /
domain adaptation
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基金
上海市人才项目（JS2024055）;上海市东方英才教师计划
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