Transformer-based Super-Resolution Reconstruction of Dongba Paintings

Naxi Dongba paintings have complex lines and rich colors. Directly using the existing methods to perform super-resolution reconstruction of low-resolution Dongba painting images in real scenes has problems such as unclear lines, excessive smoothness in local areas and lack of details. To solve the above problems, we propose a Transformer-based super-resolution reconstruction method for Dongba paintings. Firstly, the generator uses a convolutional layer and the residual dense Swin Transformer blocks to extract the shallow and deep features of the Dongba painting image, and fuses the features through the reconstruction module to reconstruct a high-resolution image. Secondly, the discriminator uses U-Net to evaluate the realness of each pixel to enhance the texture details of the reconstructed image. Finally, the generator is trained using pixel loss, perceptual loss and adversarial loss to generate natural and clear Dongba painting images. Compared with the other 8 methods on the self-built Dongba painting testing set, the results show that the reconstruction results of the proposed method have better visual effects. The average PIQE is 22.749 3, 20.264 9 and 18.378 0, and the average ENIQA is 0.091 7, 0.063 9 and 0.068 4 at magnifications of 2×, 4×, and 8×, respectively, all of which are superior to other methods. The proposed method has good scalability and achieves clearer results on natural images as well.