Multi-stage Progressive Reasoning for Dunhuang Murals Inpainting
Wenjie Liu Baokai Liu Shiqiang Du Yuqing Shi Jiacheng Li Jianhua Wang
Dunhuang murals suffer from fading, breakage, surface brittleness and
extensive peeling affected by prolonged environmental erosion. Image inpainting
techniques are widely used in the field of digital mural inpainting. Generally
speaking, for mural inpainting tasks with large area damage, it is challenging
for any image inpainting method. In this paper, we design a multi-stage
progressive reasoning network (MPR-Net) containing global to local receptive
fields for murals inpainting. This network is capable of recursively inferring
the damage boundary and progressively tightening the regional texture
constraints. Moreover, to adaptively fuse plentiful information at various
scales of murals, a multi-scale feature aggregation module (MFA) is designed to
empower the capability to select the significant features. The execution of the
model is similar to the process of a mural restorer (i.e., inpainting the
structure of the damaged mural globally first and then adding the local texture
details further). Our method has been evaluated through both qualitative and
quantitative experiments, and the results demonstrate that it outperforms
state-of-the-art image inpainting methods.