Title: Embroidery artefact image restoration technology based on improved DenseNet and GAN

Authors: Jiazhao Lin

Addresses: School of Design, Hainan Vocational University of Science and Technology, Haikou, 571126, China

Abstract: Embroidery cultural relics often suffer from missing stitches and colour fading due to environmental and human factors. Traditional manual restoration is time-consuming (over 200 hours per piece) and prone to secondary damage. Existing deep learning methods struggle with structural distortion (>15%) and poor semantic consistency in complex textures like gold/silver threads and three-dimensional embroidery. To address these issues, we propose an advanced embroidery image restoration method. A DenseNetwork with channel and spatial attention achieves 94.25% accuracy, 93.47% recall, and 96.59% specificity for classification. Restoration uses an improved GAN with dilated convolutions, attention modules, a mask-guided discriminator, and joint loss. On datasets with 20-30% masking, the SSIM reached 0.971 (vs. 0.873 for traditional GANs). At 40-50% masking, the FID dropped to 17.33 (vs. 20.14). The model is efficient, requiring only 10.52G FLOPs, 5.58M parameters, and 0.62s per image. This method enables high-quality, efficient restoration of embroidery artefacts.

Keywords: image restoration; DenseNet; GAN; embroidery; classification.

DOI: 10.1504/IJICA.2025.148636

International Journal of Innovative Computing and Applications, 2025 Vol.15 No.3, pp.192 - 205

Received: 26 May 2025
Accepted: 16 Jul 2025
Published online: 16 Sep 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article