ConvSRGAN: Super-Resolution Inpainting of Traditional Chinese Paintings

Abstract

Existing image super-resolution methods have made remarkable advancements in enhancing the visual quality of real-world images. However, when it comes to restoring Chinese paintings, these methods encounter unique challenges. This is primarily due to the difficulty in preserving intricate non-realistic details and capturing complex semantic information with high dimensionality. Moreover, the preservation of the original artwork’s distinct style and subtle artistic nuances fur- ther amplifies this complexity. To address these challenges and effectively restore traditional Chinese paintings, we propose a Convolutional Super-Resolution Gen- erative Adversarial Network (ConvSRGAN) framework specifically designed for Chinese landscape painting super-resolution. Our ConvSRGAN employs several Enhanced Adaptive Residual Modules (EARM) to delve deeply into multi- scale feature extraction in images, incorporating an Enhanced High-Frequency Retention Module (EHRM) that leverages an Adaptive Deep Convolution Block (ADCB) to capture fine-grained high-frequency details across multiple levels. By combining the Multi-Scale Structural Similarity (MS-SSIM) loss with con- ventional losses, our ConvSRGAN ensures that the model produces outputs with improved fidelity to the original image’s texture and structure. Experimen- tal validation demonstrates significant qualitative and quantitative results when 1 processing traditional paintings and murals datasets, particularly excelling in high-definition reconstruction tasks for landscape paintings. The reconstruction effect showcases enhanced visual fidelity and liveliness, thus affirming the effec- tiveness and applicability of our approach in cultural heritage preservation and restoration.