面向色盲的色彩通用设计神经网络
Color Universal Design Neural Network for the Color Vision Deficiencies
AI总结：
针对色盲者无法识别图像中相邻失真颜色信息的问题，提出卷积深度神经网络CUD-Net，通过四步流程及共轭损失函数生成保持色彩与对比度稳定的图像，代码已开源。
摘要：
图像信息应能被所有人视觉理解，包括色盲患者。然而，若色盲者感知到的失真颜色与相邻物体颜色相近，这类信息便无法识别。本文旨在提出一种色彩通用设计网络（CUD-Net），生成能被色盲者视觉理解的图像。CUD-Net是卷积深度神经网络，通过回归分段线性函数的节点并对每张图像使用特定滤波器，实现输入图像的色彩保留与区分。生成色盲用CUD图像需四步：首先，基于色彩专家制定的特定标准优化CUD数据集；其次，通过针对色盲视觉的预处理扩展输入图像信息；再次，采用多模态融合架构组合特征并处理扩展图像；最后，针对数据集产生的一对多问题，基于模型预测图像的构成提出共轭损失函数。该方法可生成保持色彩与对比度稳定性的高质量CUD图像，代码已在GitHub仓库发布。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
