GENERATIVE ADVERSARIAL NETWORKS FOR PATTERN RECONSTRUCTION - Aloni, Sukhada Shashank Aloni; Khandarkar, Kiran Ramesh; Nandini, D. Usha; Yadav, Pooja; Gupta, Mridula; V. Sathiya; Bhise, Suhas (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Pattern reconstruction of patterns has become one of the most important tasks of computer vision, preservation of digital heritage, automation of textile, and generative design. Their capability to learn the complex data distributions through a training process of the Generative Adversarial Networks (GANs) offers strong infrastructure to learn the reconstruction of the structural, geometric, or textual motifs using incomplete, noisy, or degraded data. The following paper includes the detailed research and application of a GAN-based pattern reconstruction framework, which makes use of adversarial learning to recover fine-grained visual patterns, and undergoes global motif consistency. The generator is designed in such a way that it generates the high-resolution structural elements and small-textural differences, but the discriminator is implemented to impose the realism with the help of adversarial control and multi-scale features. Combining these networks, collectively they reconstruct errors more efficiently, have greater pattern consistency and less feature fidelity to a wide pattern space, including folk art, textiles, mosaics and ornamental graphics. The optimization principles of GAN, loss objective of the structural accuracy, and stability issues are described in detail. In the proposed system architecture, reconstruction loss, perceptual loss, and adversarial loss are combined to compromise between fidelity and realism. The experimental findings indicate there are great enhancements in structural similarity, PSNR and detail recovery in comparison with baseline autoencoders and classical inpainting models. Moreover, in qualitative measurements, there are improved preservations of edges, color gradients, and symmetries of motifs. Major drawbacks, such as the instability of training, large computational costs, and failure to reconstruct extremely complex motifs are mitigated with future research directions, such as hybrid