ShapeMoir\'e: Channel-Wise Shape-Guided Network for Image Demoir\'eing

TL;DR

ShapeMoiré introduces a shape-guided network leveraging channel-wise shape features to effectively address moiré patterns in images, outperforming existing methods without extra computational costs.

Contribution

The paper proposes a novel shape-based approach for image demoiréing, capturing channel-wise and global shape features to improve performance over traditional CNN methods.

Findings

Achieves state-of-the-art PSNR on four datasets.

Effectively models moiré patterns using shape features.

No additional parameters or inference overhead.

Abstract

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 \emph{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 new method, ShapeMoir\'e, for 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 any 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.