UNet-GAN-based design and implementation of art creation AIDS

The restoration of ancient murals and heritage paintings is, however, a serious undertaking as they are riddled with extreme deterioration, lost areas, lost textures, and disjointed structure. The current deep learning methods fail to preserve fine-grained details at the traditional methods of restoration, whereas the global coherence and perceptual realism are usually not preserved. In order to solve this issue, the current work suggests a hybrid model of Dense U-Net + GAN that would be used to perform high-quality mural inpainting and art reconstruction. This model combines dense feature propagation and adversarial learning to improve both the texture recovery, structural continuity, and color fidelity. The suggested framework was conditioned on the MuralDH dataset and assessed in terms of standard measures. Experimental findings indicate that our technique attains PSNR = 34.8 dB, SSIM = 0.962, LPIPS = 0.081, and FID = 8.7, which are better than the state-of-the-art methods, which include LaMa, EdgeConnect, and DeepFillv2. Further external validation of ancient fresco and wall-painting datasets shows great generalization, with steady increases of +12-18% in the accuracy of restoration. On balance, the Dense U-Net + GAN model offers a sufficiently effective and efficient tool in the field of digital heritage preservation and art recovery with the assistance of AI.