Image Inpainting for Digital Dunhuang Murals Using Partial Convolutions and Sliding Window Method

Abstract

It is a difficult and challenging task to restore the digital murals to a visually pleasant result, and even the result is similar to the original murals without corruption. In this paper, to address the above problem, we propose an image inpainting strategy called PCSW for digital Dunhuang murals using partial convolutions and sliding window method. Specially, a deep neural network based on partial convolutions is used as the underlying model for image inpainting. Because the murals are somewhat damaged or even large areas are missing, in addition, digital murals are large and high resolution, it is unreasonable and impractical to use the original digital murals for training and then restoring the missing areas. Therefore, a data augmentation method based on sliding window technique is applied to increase samples and then improve the model accuracy. Experimental results have shown that the proposed strategy has a certain effect on the restoration of digital Dunhuang murals.