Strip Attention for Image Restoration
Authors: Yuning Cui, Yi Tao, Luoxi Jing, Alois Knoll
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments To verify the effectiveness of our SANet, we conduct extensive experiments on several image restoration tasks, including single-image defocus deblurring (DPDD [Abuolaim and Brown, 2020]), image dehazing (RESIDE [Li et al., 2018]), and image desnowing (CSD [Chen et al., 2021]). |
| Researcher Affiliation | Academia | 1School of Computation, Information and Technology, Technical University of Munich, Germany 2MIT Universal Village Program, USA 3School of Computer Science, Peking University, China |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found. |
| Open Source Code | Yes | The code is available at https://github.com/c-yn/SANet. |
| Open Datasets | Yes | We train the network on the RESIDE [Li et al., 2018] dataset and test on the SOTS [Li et al., 2018] dataset. The results are reported in Table 1. Our SANet achieves better performance with lower complexity than most approaches. Particularly on the SOTS-Outdoor dataset, SANet yields a 2.83 d B performance gain over the expensive Transformer model De Hamer [Guo et al., 2022] with only 76% MACs and 3% parameters. |
| Dataset Splits | No | We train the proposed network via Adam optimizer with β1 = 0.9, β2 = 0.999. The initial learning rate is set to 1e 4 and reduced to 1e 6 gradually with the cosine annealing. The batch size is set as 8 for the RESIDE-Outdoor [Li et al., 2018] dataset and 4 for others. Models are trained on the patch size of 256 256. We adopt only horizontal flips for data augmentation. We choose k1 = 7 and k2 = 11 in Eq. 5. According to the task complexity, we deploy varying numbers of residual blocks N in each scale for different tasks, i.e., N = 4 for image dehazing and desnowing, and N = 16 for image defocus deblurring. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) were found. |
| Experiment Setup | Yes | We train the proposed network via Adam optimizer with β1 = 0.9, β2 = 0.999. The initial learning rate is set to 1e 4 and reduced to 1e 6 gradually with the cosine annealing. The batch size is set as 8 for the RESIDE-Outdoor [Li et al., 2018] dataset and 4 for others. Models are trained on the patch size of 256 256. We adopt only horizontal flips for data augmentation. We choose k1 = 7 and k2 = 11 in Eq. 5. According to the task complexity, we deploy varying numbers of residual blocks N in each scale for different tasks, i.e., N = 4 for image dehazing and desnowing, and N = 16 for image defocus deblurring. |