BinaryDemoire: Moir\'e-Aware Binarization for Image Demoir\'eing
Zheng Chen, Zhi Yang, Xiaoyang Liu, Weihang Zhang, Mengfan Wang, Yifan Fu, Linghe Kong, Yulun Zhang

TL;DR
BinaryDemoire introduces a frequency-aware binarization framework for image demoiréing, effectively reducing model size while maintaining high restoration quality by explicitly modeling moiré artifacts.
Contribution
The paper proposes a novel moiré-aware binary gate and a shuffle-grouped residual adapter to improve binarized demoiréing models, addressing the challenge of frequency-dependent degradations.
Findings
Outperforms existing binarization methods on four benchmarks.
Effectively captures frequency structure of moiré artifacts.
Maintains high-quality image restoration with reduced model complexity.
Abstract
Image demoir\'eing aims to remove structured moir\'e artifacts in recaptured imagery, where degradations are highly frequency-dependent and vary across scales and directions. While recent deep networks achieve high-quality restoration, their full-precision designs remain costly for deployment. Binarization offers an extreme compression regime by quantizing both activations and weights to 1-bit. Yet, it has been rarely studied for demoir\'eing and performs poorly when naively applied. In this work, we propose BinaryDemoire, a binarized demoir\'eing framework that explicitly accommodates the frequency structure of moir\'e degradations. First, we introduce a moir\'e-aware binary gate (MABG) that extracts lightweight frequency descriptors together with activation statistics. It predicts channel-wise gating coefficients to condition the aggregation of binary convolution responses. Second, we…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image and Signal Denoising Methods · Advanced Image Fusion Techniques
