ShapeMoir\'e: Channel-Wise Shape-Guided Network for Image Demoir\'eing
<|reference_start|>ShapeMoir\'e: Channel-Wise Shape-Guided Network for Image Demoir\'eing: 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.<|reference_end|>
@article{cao2024shapemoir\'e:,
    title={ShapeMoir\'e: Channel-Wise Shape-Guided Network for Image Demoir\'eing},
    author={Jinming Cao, Sicheng Shen, Qiu Zhou, Yifang Yin, Yangyan Li and Roger
  Zimmermann},
    journal={arXiv preprint arXiv:2404.18155},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18155},
    primaryClass={cs.CV}
}