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@article{181670,
        author = {Ms. Kimaya Churi and Mrs. Bhakti Patil},
        title = {Image Colorization Using computer vision and machin learning techniques : A systematic review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {4962-4966},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181670},
        abstract = {Image colorization is the process of adding plausible color information to grayscale images, with applications in image restoration, digital media, and historical photo enhancement. Recent advancements in deep learning have enabled more realistic and context-aware colorization. In this research, we explore the use of Conditional Generative Adversarial Networks (cGANs) for image colorization and conduct a comprehensive performance evaluation against traditional and state-of-the-art deep learning methods. We assess the effectiveness of cGANs using quantitative metrics such as PSNR, SSIM, and FID, as well as qualitative visual results and user studies.},
        keywords = {Image colorization, Machine learning, Computer Vision},
        month = {June},
        }