FreqFormer: Frequency-aware Transformer for Lightweight Image Super-resolution
Authors: Tao Dai, Jianping Wang, Hang Guo, Jinmin Li, Jinbao Wang, Zexuan Zhu
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on public datasets demonstrate the superiority of our Freq Former over state-of-the-art SR methods in terms of both quantitative metrics and visual quality. |
| Researcher Affiliation | Academia | Tao Dai1,2 , Jianping Wang1 , Hang Guo3 , Jinmin Li3 , Jinbao Wang1,2, , Zexuan Zhu1,2, 1College of Computer Science and Software Engineering, Shenzhen University 2National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University 3Tsinghua Shenzhen International Graduate School, Tsinghua University |
| Pseudocode | No | The paper includes architectural diagrams (Figure 2) but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and models are available at https://github.com/JPWang-CS/FreqFormer. |
| Open Datasets | Yes | Two training datasets, DIV2K [Lim et al., 2017] and Flickr2K [Radu Timofte and Zhang, 2017], were used for model training. |
| Dataset Splits | No | The paper describes training datasets (DIV2K, Flickr2K) and benchmark testing datasets (Set5, Set14, BSD100, Urban100, Manga109), but does not explicitly provide details about a separate validation dataset split. |
| Hardware Specification | Yes | Additionally, the model was trained using the PyTorch toolkit on 4 NVIDIA 3090 GPUs. |
| Software Dependencies | No | The paper mentions “PyTorch toolkit” but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | In our training setup, the model was configured with a patch size of 64x64, and a batch size of 32. The training process comprised 500,000 iterations, with an initial learning rate of 2e-4. The learning rate was halved at specific milestones: [250K, 400K, 450K, 475K]. Data augmentation techniques, including random horizontal flipping, and rotations at 90, 180, and 270, were applied to the training set. For optimization, the Adam optimizer was employed with β1 = 0.9 and β2 = 0.99 to minimize the L1 loss. |