Image Color Correction for Color Vision Deficiency Using ResNet and CycleGAN

Color blindness is a visual impairment that limits an individual's ability to accurately perceive certain colors, particularly red, green, or blue. This condition can hinder daily tasks, especially when color identification is crucial. This study proposes a color correction system designed to enhance color perception for individuals with color vision deficiency (CVD), focusing on important visual areas within an image. The method involves converting RGB images into LMS color space, simulating types of color blindness (protanopia, deuteranopia, and tritanopia), detecting visually important regions using a saliency mask, applying color correction through a ResNet-based deep learning model, and performing a reverse transformation back to RGB using a CycleGAN. A total of 5,020 images were used for evaluation, and the proposed system achieved an average Root Mean Square (RMS) error of 0.0212. The Mean Absolute Error (MAE) ranged from 0.1541 to 0.5582 depending on the CVD type. In addition to quantitative evaluation, qualitative validation was conducted through a GUI-based user test involving 10 color blind participants. The system showed the highest effectiveness for deuteranopia with a color recognition accuracy of 71.666%, followed by tritanopia at 59.666% and protanopia at 46.500%. These results indicate that the proposed system offers significant potential in aiding individuals with CVD to better interpret color-based information, especially in visually important regions of an image. Future work may explore broader datasets and alternative deep learning architectures to further improve accuracy and adaptability.