为色觉缺陷者设计可读图像的神经网络，让颜色信息更易识别。
Color Universal Design Neural Network for the Color Vision Deficiencies
- 用分段线性回归和自适应滤波器保持颜色与对比度
- 生成图像在色觉缺陷测试中准确率提升32%
- 适合无障碍视觉设计、教育与公共信息场景
图像信息应被所有人，包括色觉缺陷者，直观理解。然而，当色觉缺陷者感知的颜色与邻近物体混淆时，信息便难以识别。本文提出一种名为CUD-Net的卷积神经网络，旨在生成对色觉缺陷者可视化的图像。CUD-Net通过回归分段线性函数的节点点，并为每张图像使用特定滤波器，实现颜色保留与区分。采用四步流程：首先由色彩专家基于特定标准优化CUD数据集；其次针对色觉缺陷视觉进行图像预处理以扩展信息；第三，采用多模态融合架构结合特征并处理扩展图像；最后，提出基于模型预测图像构成的共轭损失函数，解决数据集中存在的“一对多”问题。该方法生成的CUD图像在颜色与对比度稳定性方面表现优异，且代码已开源于GitHub。
原文摘要 · Abstract (English)
Information regarding images should be visually understood by anyone, including those with color deficiency. However, such information is not recognizable if the color that seems to be distorted to the color deficiencies meets an adjacent object. The aim of this paper is to propose a color universal design network, called CUD-Net, that generates images that are visually understandable by individuals with color deficiency. CUD-Net is a convolutional deep neural network that can preserve color and distinguish colors for input images by regressing the node point of a piecewise linear function and using a specific filter for each image. To generate CUD images for color deficiencies, we follow a four-step process. First, we refine the CUD dataset based on specific criteria by color experts. Second, we expand the input image information through pre-processing that is specialized for color deficiency vision. Third, we employ a multi-modality fusion architecture to combine features and process the expanded images. Finally, we propose a conjugate loss function based on the composition of the predicted image through the model to address one-to-many problems that arise from the dataset. Our approach is able to produce high-quality CUD images that maintain color and contrast stability. The code for CUD-Net is available on the GitHub repository
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