Sketch-Guided Conditional Generative Adversarial Network for Restoration and Design Generation of Historical Building Images

Restoring damaged historical building images requires maintaining structural integrity, preserving authentic styles, and supporting interactive flexibility.Existing conditional generative adversarial networks (cGANs) often fall short in enforcing structural constraints, leveraging frequency-domain features, and decoupling structure from texture, limiting their effectiveness for high-precision restoration and interactive design.To address these challenges, we propose a sketch-guided cGAN with multi-scale frequency-aware perception, enabling precise restoration and personalized design generation for historical building images.The network adopts a dual-encoder-shared-decoder architecture to enforce structural constraints, introduces a frequency-adaptive feature modulation module to overcome the limitations of spatial convolutions in balancing structure and texture, and implements a structure-texture decoupled generation strategy to synergistically achieve style transfer and structural fidelity.Furthermore, a multi-scale trident discriminator is designed to enhance visual realism and style consistency of the generated results.Experiments on both a self-constructed historical building damage dataset and public datasets demonstrate that the proposed method outperforms state-of-the-art approaches in metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and perceptual similarity, while effectively supporting interactive design.The generated results conform to historical architectural style standards.This approach provides a novel technical pathway for image restoration and digital design of historical buildings, with significant theoretical and practical implications.