Progressive Multilevel Feature Inpainting Algorithm for Chinese Ancient Paintings

There are some problems in the field of Chinese ancient painting images, such as inconsistent repaired image contents and obvious artificial traces, loss of detailed information in the repaired area, and blurred repaired content. To address these problems, we propose to use the multi-level semantic features of the painting image to repair ancient Chinese paintings gradually. The revision process of ancient Chinese painting is divided into five different stages, which are to repair the ancient painting image gradually from the macro, meso, and micro semantic levels. The repair model first pays attention to the high-level semantic information and large-scale structural information and then gradually shifts its attention to more and more fine scales without learning the information of all scales at the same time. The repair model gradually changes from the high-level semantic features of ancient paintings to the low-level semantic features. This method not only makes the repair process more orderly but also makes use of the knowledge learned in the previous stage to simplify the repair in the subsequent stage. Based on 197 high-definition digital ancient paintings by 11 writers, four data sets of landscape painting, street view painting, flower, and bird painting, and figure painting are made. Experiments on the above data sets show that the algorithm in this paper is better than the one-step repair algorithms in PSNR, SSIM, IS, FID.