Learning Image Demoireing from Unpaired Real Data
Yunshan Zhong Yuyao Zhou Yuxin Zhang Fei Chao Rongrong Ji
This paper focuses on addressing the issue of image demoireing. Unlike the
large volume of existing studies that rely on learning from paired real data,
we attempt to learn a demoireing model from unpaired real data, i.e., moire
images associated with irrelevant clean images. The proposed method, referred
to as Unpaired Demoireing (UnDeM), synthesizes pseudo moire images from
unpaired datasets, generating pairs with clean images for training demoireing
models. To achieve this, we divide real moire images into patches and group
them in compliance with their moire complexity. We introduce a novel moire
generation framework to synthesize moire images with diverse moire features,
resembling real moire patches, and details akin to real moire-free images.
Additionally, we introduce an adaptive denoise method to eliminate the
low-quality pseudo moire images that adversely impact the learning of
demoireing models. We conduct extensive experiments on the commonly-used FHDMi
and UHDM datasets. Results manifest that our UnDeM performs better than
existing methods when using existing demoireing models such as MBCNN and
ESDNet-L. Code: https://github.com/zysxmu/UnDeM