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arXiv · 2506.18437

Frequency-Domain Fusion Transformer for Image Inpainting

Sijin He·Guangfeng Lin·Tao Li·Yajun Chen
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Abstract

Image inpainting plays a vital role in restoring missing image regions and supporting high-level vision tasks, but traditional methods struggle with complex textures and large occlusions. Although Transformer-based approaches have demonstrated strong global modeling capabilities, they often fail to preserve high-frequency details due to the low-pass nature of self-attention and suffer from high computational costs. To address these challenges, this paper proposes a Transformer-based image inpainting method incorporating frequency-domain fusion. Specifically, an attention mechanism combining wavelet transform and Gabor filtering is introduced to enhance multi-scale structural modeling and detail preservation. Additionally, a learnable frequency-domain filter based on the fast Fourier transform is designed to replace the feedforward network, enabling adaptive noise suppression and detail retention. The model adopts a four-level encoder-decoder structure and is guided by a novel loss strategy to balance global semantics and fine details. Experimental results demonstrate that the proposed method effectively improves the quality of image inpainting by preserving more high-frequency information.

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Sijin He, Guangfeng Lin, Tao Li, Yajun Chen. 2025-06-23. Frequency-Domain Fusion Transformer for Image Inpainting. https://arxiv.org/abs/2506.18437

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