ARIN: Adaptive Resampling and Instance Normalization for Robust Blind
  Inpainting of Dunhuang Cave Paintings
Alexander Schmidt Prathmesh Madhu Andreas Maier Vincent Christlein Ronak Kosti
Image enhancement algorithms are very useful for real world computer vision
tasks where image resolution is often physically limited by the sensor size.
While state-of-the-art deep neural networks show impressive results for image
enhancement, they often struggle to enhance real-world images. In this work, we
tackle a real-world setting: inpainting of images from Dunhuang caves. The
Dunhuang dataset consists of murals, half of which suffer from corrosion and
aging. These murals feature a range of rich content, such as Buddha statues,
bodhisattvas, sponsors, architecture, dance, music, and decorative patterns
designed by different artists spanning ten centuries, which makes manual
restoration challenging. We modify two different existing methods (CAR, HINet)
that are based upon state-of-the-art (SOTA) super resolution and deblurring
networks. We show that those can successfully inpaint and enhance these
deteriorated cave paintings. We further show that a novel combination of CAR
and HINet, resulting in our proposed inpainting network (ARIN), is very robust
to external noise, especially Gaussian noise. To this end, we present a
quantitative and qualitative comparison of our proposed approach with existing
SOTA networks and winners of the Dunhuang challenge. One of the proposed
methods HINet) represents the new state of the art and outperforms the 1st
place of the Dunhuang Challenge, while our combination ARIN, which is robust to
noise, is comparable to the 1st place. We also present and discuss qualitative
results showing the impact of our method for inpainting on Dunhuang cave
images.