Color Constancy

Color constancy research aims to understand and replicate the human visual system's ability to perceive consistent object colors despite varying lighting conditions. Current research focuses on developing robust algorithms and models, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and diffusion models, to achieve accurate color constancy across diverse scenarios, such as multi-illuminant scenes and cross-sensor image processing. These advancements are improving image and video processing, impacting applications ranging from medical image analysis (e.g., virtual staining) to image compression and colorization, and enhancing the understanding of human color perception. Furthermore, research is exploring the use of self-supervised learning and incorporating semantic information to improve model performance and robustness.