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.
Abstract (translated)
图像修复在恢复缺失的图像区域和支持高级视觉任务方面扮演着至关重要的角色，但传统方法在处理复杂纹理和大面积遮挡时往往表现不佳。虽然基于Transformer的方法展示了强大的全局建模能力，但由于自注意力机制的低通特性，它们通常无法很好地保留高频细节，并且计算成本较高。为了解决这些挑战，本文提出了一种结合频率域融合的基于Transformer的图像修复方法。具体来说，引入了一个结合小波变换和Gabor滤波器的注意机制，以增强多尺度结构建模和细节保留能力。此外，还设计了一种基于快速傅里叶变换（FFT）的可学习频域滤波器来替代前馈网络，从而实现自适应噪声抑制和细节保持功能。 该模型采用了四级编码-解码架构，并通过一种新的损失策略来进行指导，以平衡全局语义信息与精细细节。实验结果表明，所提出的方法有效提高了图像修复的质量，尤其是在保留更多高频信息方面表现出色。
URL
https://arxiv.org/abs/2506.18437
