Color Space

Color space research focuses on representing and manipulating color information for various applications, aiming to bridge the gap between digital representations and human color perception. Current research emphasizes developing improved color difference metrics, exploring the effectiveness of different color spaces (e.g., RGB, LAB, YUV) for specific tasks like image colorization, enhancement, and denoising, often employing deep learning models such as convolutional neural networks and transformers. These advancements have significant implications for diverse fields, including computer vision, image processing, assistive technologies for color vision deficiency, and even astronomical image analysis, by improving accuracy, efficiency, and perceptual fidelity in color-related applications.