  Photographing optoelectronic displays often introduces unwanted moir\'e
patterns due to analog signal interference between the pixel grids of the
display and the camera sensor arrays. This work identifies two problems that
are largely ignored by existing image demoir\'eing approaches: 1) moir\'e
patterns vary across different channels (RGB); 2) repetitive patterns are
constantly observed. However, employing conventional convolutional (CNN) layers
cannot address these problems. Instead, this paper presents the use of our
recently proposed Shape concept. It was originally employed to model consistent
features from fragmented regions, particularly when identical or similar
objects coexist in an RGB-D image. Interestingly, we find that the Shape
information effectively captures the moir\'e patterns in artifact images.
Motivated by this discovery, we propose a ShapeMoir\'e method to aid in image
demoir\'eing. Beyond modeling shape features at the patch-level, we further
extend this to the global image-level and design a novel Shape-Architecture.
Consequently, our proposed method, equipped with both ShapeConv and
Shape-Architecture, can be seamlessly integrated into existing approaches
without introducing additional parameters or computation overhead during
inference. We conduct extensive experiments on four widely used datasets, and
the results demonstrate that our ShapeMoir\'e achieves state-of-the-art
performance, particularly in terms of the PSNR metric. We then apply our method
across four popular architectures to showcase its generalization capabilities.
Moreover, our ShapeMoir\'e is robust and viable under real-world demoir\'eing
scenarios involving smartphone photographs.
