Color Universal Design Neural Network for the Color Vision Deficiencies

TL;DR

This paper introduces CUD-Net, a deep neural network designed to generate images that are understandable by individuals with color vision deficiencies, improving accessibility through specialized processing and a novel loss function.

Contribution

CUD-Net is a new neural network architecture that preserves color and contrast for color-deficient viewers, utilizing a multi-modality fusion and conjugate loss for improved image generation.

Findings

High-quality CUD images with stable color and contrast.

Effective handling of one-to-many mapping problems in color deficiency images.

Code available on GitHub for reproducibility.

Abstract

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