J Opt Soc Am A Opt Image Sci Vis. 2023 Mar 1;40(3):A48-A56. doi: 10.1364/JOSAA.479451.
Color constancy refers to our capacity to see consistent colors under different illuminations. In computer vision and image processing, color constancy is often approached by explicit estimation of the scene's illumination, followed by an image correction. In contrast, color constancy in human vision is typically measured as the capacity to extract color information about objects and materials in a scene consistently throughout various illuminations, which goes beyond illumination estimation and might require some degree of scene and color understanding. Here, we pursue an approach with deep neural networks that tries to assign reflectances to individual objects in the scene. To circumvent the lack of massive ground truth datasets labeled with reflectances, we used computer graphics to render images. This study presents a model that recognizes colors in an image pixel by pixel under different illumination conditions.
颜色恒常性是指我们在不同光照条件下看到一致颜色的能力。在计算机视觉和图像处理中，颜色恒常性通常通过显式估计场景照明来实现，然后进行图像校正。相比之下，人类视觉中的颜色恒常性通常被测量为从不同光照条件下的场景中一致地提取物体和材料的颜色信息的能力，这超出了照明估计的范围，可能需要一定程度的场景和颜色理解。在这里，我们采用了一种基于深度神经网络的方法，尝试为场景中的单个物体分配反射率。为了避免缺乏大量带有反射率标签的地面实况数据集，我们使用计算机图形学来渲染图像。本研究提出了一种在不同光照条件下逐像素识别图像颜色的模型。
