From shades to vibrance: a comprehensive review of modern image colorization techniques

Image colorization has become a significant task in computer vision, addressing the challenge of transforming grayscale images into realistic, vibrant color outputs. Recent advancements leverage deep learning techniques, ranging from generative adversarial networks (GANs) to diffusion models, and integrate semantic understanding, multi-scale features, and user-guided controls. This review explores state-of-the-art methodologies, highlighting innovative components such as semantic class distribution learning, bidirectional temporal fusion, and instance-aware frameworks. Evaluation metrics, including PSNR, FID, and task-specific measures, ensure a comprehensive assessment of performance. Despite remarkable progress, challenges like multimodal uncertainty, computational cost, and generalization remain. This paper provides a thorough analysis of existing approaches, offering insights into their contributions, limitations, and future directions in automated image colorization.