Ultra-High-Definition Image Deblurring via Multi-scale Cubic-Mixer
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| Main Authors: | , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866917892679794688 |
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| author | Chen, Xingchi Jia, Xiuyi Zheng, Zhuoran |
| author_facet | Chen, Xingchi Jia, Xiuyi Zheng, Zhuoran |
| contents | Currently, transformer-based algorithms are making a splash in the domain of image deblurring. Their achievement depends on the self-attention mechanism with CNN stem to model long range dependencies between tokens. Unfortunately, this ear-pleasing pipeline introduces high computational complexity and makes it difficult to run an ultra-high-definition image on a single GPU in real time. To trade-off accuracy and efficiency, the input degraded image is computed cyclically over three dimensional ($C$, $W$, and $H$) signals without a self-attention mechanism. We term this deep network as Multi-scale Cubic-Mixer, which is acted on both the real and imaginary components after fast Fourier transform to estimate the Fourier coefficients and thus obtain a deblurred image. Furthermore, we combine the multi-scale cubic-mixer with a slicing strategy to generate high-quality results at a much lower computational cost. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art deblurring approaches on the several benchmarks and a new ultra-high-definition dataset in terms of accuracy and speed. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2206_03678 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Ultra-High-Definition Image Deblurring via Multi-scale Cubic-Mixer Chen, Xingchi Jia, Xiuyi Zheng, Zhuoran Computer Vision and Pattern Recognition Currently, transformer-based algorithms are making a splash in the domain of image deblurring. Their achievement depends on the self-attention mechanism with CNN stem to model long range dependencies between tokens. Unfortunately, this ear-pleasing pipeline introduces high computational complexity and makes it difficult to run an ultra-high-definition image on a single GPU in real time. To trade-off accuracy and efficiency, the input degraded image is computed cyclically over three dimensional ($C$, $W$, and $H$) signals without a self-attention mechanism. We term this deep network as Multi-scale Cubic-Mixer, which is acted on both the real and imaginary components after fast Fourier transform to estimate the Fourier coefficients and thus obtain a deblurred image. Furthermore, we combine the multi-scale cubic-mixer with a slicing strategy to generate high-quality results at a much lower computational cost. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art deblurring approaches on the several benchmarks and a new ultra-high-definition dataset in terms of accuracy and speed. |
| title | Ultra-High-Definition Image Deblurring via Multi-scale Cubic-Mixer |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2206.03678 |