用频域先验提升图像去模糊模型的泛化能力
Frequency-domain Learning with Kernel Prior for Blind Image Deblurring
- 在频域中融合图像模糊核先验信息
- 在多个数据集上超越现有最佳方法
- 适合需要强泛化能力的去模糊任务
尽管在多个数据集上表现优异，许多基于深度学习的图像去模糊方法在域外数据上泛化能力有限，这可能源于对特定领域数据集的依赖。为此，我们主张将与图像上下文无关的模糊核先验引入深度学习方法。为有效融合该先验，我们借鉴传统去模糊算法在频域进行反卷积的思路，提出频率融合模块（Frequency Integration Module, FIM），并将其与基于频率的去模糊Transformer网络结合。实验表明，该方法在多个盲图像去模糊任务中优于当前最优方法，展现出出色的泛化性能。源代码即将发布。
原文摘要 · Abstract (English)
While achieving excellent results on various datasets, many deep learning methods for image deblurring suffer from limited generalization capabilities with out-of-domain data. This limitation is likely caused by their dependence on certain domain-specific datasets. To address this challenge, we argue that it is necessary to introduce the kernel prior into deep learning methods, as the kernel prior remains independent of the image context. For effective fusion of kernel prior information, we adopt a rational implementation method inspired by traditional deblurring algorithms that perform deconvolution in the frequency domain. We propose a module called Frequency Integration Module (FIM) for fusing the kernel prior and combine it with a frequency-based deblurring Transfomer network. Experimental results demonstrate that our method outperforms state-of-the-art methods on multiple blind image deblurring tasks, showcasing robust generalization abilities. Source code will be available soon.
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