Effective cross-sensor color constancy using a dual-mapping strategy

Shuwei Yue and Minchen Wei
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Abstract

Deep neural networks (DNNs) have been widely used for illuminant estimation, which commonly requires great efforts to collect sensor-specific data. In this paper, we propose a dual-mapping strategy—the DMCC method. It only requires the white points captured by the training and testing sensors under a D65 condition to reconstruct the image and illuminant data, and then maps the reconstructed image into sparse features. These features, together with the reconstructed illuminants, were used to train a lightweight multi-layer perceptron (MLP) model, which can be directly used to estimate the illuminant for the testing sensor. The proposed model was found to have performance comparable to other state-of-the-art methods, based on the three available datasets. Moreover, the smaller number of parameters, faster speed, and not requiring data collection using the testing sensor make it ready for practical deployment. This paper is an extension of Yue and Wei [Color and Imaging Conference (2023)], with more detailed results, analyses, and discussions.
使用双映射策略实现有效的跨传感器色彩恒定性
深度神经网络(DNN)已被广泛应用于光照度估计,这通常需要花费大量精力收集传感器特定数据。在本文中,我们提出了一种双映射策略--DMCC 方法。它只需要在 D65 条件下由训练和测试传感器捕获的白点来重建图像和照度数据,然后将重建图像映射为稀疏特征。这些特征与重建的光照度一起用于训练轻量级多层感知器(MLP)模型,该模型可直接用于估计测试传感器的光照度。根据现有的三个数据集,发现所提出的模型与其他最先进的方法性能相当。此外,该模型参数数量少、速度快,而且不需要使用测试传感器收集数据,因此可用于实际部署。本文是对 Yue 和 Wei [Color and Imaging Conference (2023)]的扩展,包含更详细的结果、分析和讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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