People with Color Vision Deficiency (CVD) cannot distinguish some color combinations under normal situations. Recoloring becomes a necessary adaptation procedure. In this paper, in order to adaptively find the key color components in an image, we first propose a self-adapting recoloring method with an Improved Octree Quantification Method (IOQM). Second, we design a screening tool of CVD datasets that is used to integrate multiple recoloring methods. Third, a CVD dataset is constructed with the help of our designed screening tool. Our dataset consists of 2313 pairs of training images and 771 pairs of testing images. Fourth, multiple GANs i.e., pix2pix-GAN [1], Cycle-GAN [2], Bicycle-GAN [3] are used for colorblind data conversion. This is the first ever effort in this research area using GANs. Experimental results show that pix2pix-GAN [1] can effectively recolor unrecognizable colors for people with CVD, and we predict that this dataset can provide some help for color blind images recoloring. Datasets and source are available at: https://github.com/doubletry/pix2pix, https://github.com/doubletry/CycleGAN and https://github.com/doubletry/BicycleGAN.

有色觉障碍（CVD）的人在正常情况下无法区分某些颜色组合。重新着色成为必要的适应过程。在本文中，为了自适应地找到图像中的关键颜色成分，我们首先提出一种使用改进的八进制量化方法（IOQM）的自适应重着色方法。其次，我们设计了CVD数据集的筛选工具，该工具用于集成多种重新着色方法。第三，借助我们设计的筛选工具构建CVD数据集。我们的数据集包括2313对训练图像和771对测试图像。第四，使用多个GAN，即pix2pix-GAN [1]，Cycle-GAN [2]，Bicycle-GAN [3]进行色盲数据转换。这是该研究领域中使用GAN的首次尝试。实验结果表明，pix2pix-GAN [1]可以有效地为CVD患者重新着色无法识别的颜色，并且我们预测该数据集可以为色盲图像重新着色提供一些帮助。数据集和源可在以下网址获得：https：//github.com/doubletry/pix2pix、https：//github.com/doubletry/CycleGAN和https://github.com/doubletry/BicycleGAN。