Image Colorization Using Self-Supervised Learning

In today’s world of data-driven artificial intelligence, the lack of labeled datasets is still a major obstacle for improving computer vision. Self-supervised learning (SSL) has emerged as an effective approach to tackle this issue by using large amounts of unlabeled data through well-designed proxy tasks. This paper explores SSL-based image colorization. By predicting the color channels from a grayscale image, the network learns meaningful and structural representations without needing manual labels. Our method combines convolutional neural networks (CNNs) and generative adversarial networks (GANs) in the Lab color space, which results in vivid, realistic, and context-aware colorizations. By optimizing both reconstruction and adversarial goals together, the model captures detailed textures and overall scene context. Experimental results show high perceptual quality and a strong ability to transfer learned features to other vision tasks. This positions SSL-based colorization as a scalable, cost-effective, and flexible solution for current AI applications.