综合利用肤色模型和感知损失的偏色去除  

Color cast removal using skin color model and perceptual loss

作　　者：桂冰洁 贾振堂 Gui Bingjie;Jia Zhentang(College of Electronical and Information Engineering,Shanghai University of Electric Power,Shanghai 201306,China)

机构地区：[1]上海电力大学电子与信息工程学院,上海201306

出　　处：《电子测量技术》2022年第23期125-131,共7页Electronic Measurement Technology

基　　金：国家自然科学基金(62105196)项目资助。

摘　　要：由于现有的颜色恒常性算法在非均匀光照、场景复杂的情况下对于偏色去除表现不好,本文提出了一种综合利用肤色模型和感知损失去除偏色的深度学习算法。该算法综合利用了肤色模型和感知损失,在计算过程中可以识别且重点关注肤色信息,更注重对图像语义的理解,而不是简单的进行像素间的计算。同时,将肤色模型与注意力机制结合,更突出了肤色区域的作用。实验结果表明,本文提出的颜色恒常计算方法能在语义层面较为准确地消除单一光照和多光照场景下图像的偏色,该算法与其他算法相比,能获得更良好的效果。Since the existing color constancy algorithms do not perform well for color cast removal in the case of non-uniform illumination and complex scenes, this paper proposes a deep learning algorithm that comprehensively uses the skin color model and perceptual loss to remove color casts. The algorithm integrates the skin color model and perceptual loss, so that can recognize and focus on skin color information in the calculation process, and pay more attention to the understanding of image semantics, rather than simple calculation between pixels. At the same time, the skin color model is combined with the attention mechanism, which highlights the role of the skin color area. The experimental results show that the color constancy calculation method proposed in this paper can accurately eliminate the color cast of images in single-illumination and multi-illumination scenes at the semantic level. Compared with other algorithms, this algorithm can achieve better results.

关 键 词：颜色恒常性 图像处理 图像恢复 感知损失 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]