Deep learning-based style transfer research for traditional woodcut paper horse art images

Abstract

Deep learning (DL)-based style transfer techniques have been employed to modify traditional woodcut paper horse art images. This art form struggles to maintain its cultural character while adapting to modern digital media. Style transfer, as a DL technique, allows for the combination of the innovative style of one image with the content of another, enabling the digital recreation of conventional aesthetics. Data collection involved the compilation of a dataset consisting of high-resolution images of traditional woodcut paper horse art. The dataset was preprocessed through normalization of image sizes, application of data augmentation techniques to enhance diversity and mitigate overfitting, and filtering of images to isolate specific features of paper horse art, including line work, shading, and texture. This research proposed the Intelligent Pied Kingfisher - Refined Convolutional Neural Network (IPK-RCNN) model, which is trained on both traditional woodcut images and modern digital art, aiming to create a hybrid style that respects traditional aesthetics while integrating contemporary stylistic elements. The style transfer process employs a Region-based Convolutional Neural Network (RCNN) that incorporates IPK optimization for the enhancement of convolutional layer weight parameters, thereby improving both efficiency and accuracy. The IPK-RCNN method effectively preserves the intricate textures and details of woodcut art while incorporating modern stylistic variations. The model achieves high-quality outputs, attaining a Peak Signal-to-Noise Ratio (PSNR) of 35.12 dB, a Structural Similarity Index (SSIM) of 0.96, an Area Under Curve (AUC) of 0.90, and an efficient execution time of 31.17 ms. This approach underscores the potential of DL in the preservation and reimagining of traditional art forms for digital platforms.