Fine-grained restoration of Mongolian patterns based on a multi-stage deep learning network

Mongolian patterns are easily damaged by various factors in the process of inheritance and preservation, and the traditional manual restoration methods are time-consuming, laborious, and costly. With the development of deep learning technology and the rapid growth of the image restoration field, the existing image restoration methods are mostly aimed at natural scene images. They do not apply to Mongolian patterns with complex line texture structures and high saturation-rich colors. In order to solve this problem, this paper proposes a Mongolian pattern restoration model with a multi-stage network. In the first stage, a pyramid context encoder network is introduced to learn the contextual features of the image for global restoration; in the second stage, a local restoration network is constructed by combining the RIC convolutional layer and the MPD down-sampling module; and in the third stage, the global refinement restoration is carried out by using the U-Net network that incorporates the attention mechanism. The experimental results show that this paper's method achieves remarkable results in the Mongolian pattern repair task, using four evaluation metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), LPIPS, and L1 Loss to compare with the existing method. The results show that this paper's method performs well in the Mongolian pattern repair task. Also, the performance of the model on public datasets verifies its wide applicability. The method in this paper provides an efficient solution for the digital restoration of Mongolian motifs and has important application prospects.