  Moire artifacts are common in digital photography, resulting from the
interference between high-frequency scene content and the color filter array of
the camera. Existing deep learning-based demoireing methods trained on large
scale datasets are limited in handling various complex moire patterns, and
mainly focus on demoireing of photos taken of digital displays. Moreover,
obtaining moire-free ground-truth in natural scenes is difficult but needed for
training. In this paper, we propose a self-adaptive learning method for
demoireing a high-frequency image, with the help of an additional defocused
moire-free blur image. Given an image degraded with moire artifacts and a
moire-free blur image, our network predicts a moire-free clean image and a blur
kernel with a self-adaptive strategy that does not require an explicit training
stage, instead performing test-time adaptation. Our model has two sub-networks
and works iteratively. During each iteration, one sub-network takes the moire
image as input, removing moire patterns and restoring image details, and the
other sub-network estimates the blur kernel from the blur image. The two
sub-networks are jointly optimized. Extensive experiments demonstrate that our
method outperforms state-of-the-art methods and can produce high-quality
demoired results. It can generalize well to the task of removing moire
artifacts caused by display screens. In addition, we build a new moire dataset,
including images with screen and texture moire artifacts. As far as we know,
this is the first dataset with real texture moire patterns.
