<![CDATA[This study evaluates CycleGAN’s performance in virtual painting restoration, with a focus on color restoration and detail reproduction. We compiled datasets categorized by art style and condition to achieve accurate restorations without altering the original reference materials. Various paintings, including those with a yellow filter, are used to create effective training datasets for CycleGAN. The model utilized cycle consistency loss and advanced data augmentation techniques. We assessed the results using PSNR, SSIM, and Color Inspector metrics, focusing on Claude Monet’s “Nasturtiums in a Blue Vase” and Hermann Corrodi’s “Prayers at Dawn.” The findings demonstrate superior color recovery and preservation of intricate details compared to other methods, confirmed through quantitative and qualitative evaluations. Key contributions include the application of CycleGAN for art restoration, model evaluation, and framework development. Practical implications extend to art conservation, digital library enhancement, art education, and broader access to restored works. Future research may explore dataset expansion, complex architectures, interdisciplinary collaboration, automated evaluation tools, and improved technologies for real-time restoration applications. In conclusion, CycleGAN holds promise for digital art conservation, with ongoing efforts aimed at integrating across fields for effective cultural preservation.]]>
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