From Attention to Frequency: Integration of Vision Transformer and FFT-ReLU for Enhanced Image Deblurring
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917548678709248 |
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| author | Mahmud, Syed Mumtahin Noki, Mahdi Mohd Hossain Majumder, Prothito Shovon Radi, Abdul Mohaimen Al Ali, Md. Haider Khan, Md. Mosaddek |
| author_facet | Mahmud, Syed Mumtahin Noki, Mahdi Mohd Hossain Majumder, Prothito Shovon Radi, Abdul Mohaimen Al Ali, Md. Haider Khan, Md. Mosaddek |
| contents | Image deblurring is vital in computer vision, aiming to recover sharp images from blurry ones caused by motion or camera shake. While deep learning approaches such as CNNs and Vision Transformers (ViTs) have advanced this field, they often struggle with complex or high-resolution blur and computational demands. We propose a new dual-domain architecture that unifies Vision Transformers with a frequency-domain FFT-ReLU module, explicitly bridging spatial attention modeling and frequency sparsity. In this structure, the ViT backbone captures local and global dependencies, while the FFT-ReLU component enforces frequency-domain sparsity to suppress blur-related artifacts and preserve fine details. Extensive experiments on benchmark datasets demonstrate that this architecture achieves superior PSNR, SSIM, and perceptual quality compared to state-of-the-art models. Both quantitative metrics, qualitative comparisons, and human preference evaluations confirm its effectiveness, establishing a practical and generalizable paradigm for real-world image restoration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10806 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Attention to Frequency: Integration of Vision Transformer and FFT-ReLU for Enhanced Image Deblurring Mahmud, Syed Mumtahin Noki, Mahdi Mohd Hossain Majumder, Prothito Shovon Radi, Abdul Mohaimen Al Ali, Md. Haider Khan, Md. Mosaddek Image and Video Processing Computer Vision and Pattern Recognition Image deblurring is vital in computer vision, aiming to recover sharp images from blurry ones caused by motion or camera shake. While deep learning approaches such as CNNs and Vision Transformers (ViTs) have advanced this field, they often struggle with complex or high-resolution blur and computational demands. We propose a new dual-domain architecture that unifies Vision Transformers with a frequency-domain FFT-ReLU module, explicitly bridging spatial attention modeling and frequency sparsity. In this structure, the ViT backbone captures local and global dependencies, while the FFT-ReLU component enforces frequency-domain sparsity to suppress blur-related artifacts and preserve fine details. Extensive experiments on benchmark datasets demonstrate that this architecture achieves superior PSNR, SSIM, and perceptual quality compared to state-of-the-art models. Both quantitative metrics, qualitative comparisons, and human preference evaluations confirm its effectiveness, establishing a practical and generalizable paradigm for real-world image restoration. |
| title | From Attention to Frequency: Integration of Vision Transformer and FFT-ReLU for Enhanced Image Deblurring |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.10806 |