Frequency Consistent Adaptation for Real World Super Resolution
Authors: Xiaozhong Ji, Guangpin Tao, Yun Cao, Ying Tai, Tong Lu, Chengjie Wang, Jilin Li, Feiyue Huang1664-1672
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that the proposed FCA improves the performance of the SR model under real-world setting achieving state-of-the-art results with high fidelity and plausible perception, thus providing a novel effective framework for realworld SR application. Experiments |
| Researcher Affiliation | Collaboration | 1National Key Lab for Novel Software Technology, Nanjing University 2Tencent Youtu Lab |
| Pseudocode | No | The paper describes the method and framework using text and diagrams, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a direct link to source code or an explicit statement about its public availability. |
| Open Datasets | Yes | For synthetic experiments, we select the widely used DIV2K (Timofte et al. 2017) dataset, including 800 training samples and 100 validation samples as benchmark. For real-world experiment, we use the DPED (Ignatov et al. 2017) dataset containing 5,614 training and 100 testing images. |
| Dataset Splits | Yes | For synthetic experiments, we select the widely used DIV2K (Timofte et al. 2017) dataset, including 800 training samples and 100 validation samples as benchmark. For real-world experiment, we use the DPED (Ignatov et al. 2017) dataset containing 5,614 training and 100 testing images. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., GPU model, CPU model, memory amount) used for running experiments, only general statements about training. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | The input size of adaptation generator is 512 512, and the scale factor is 4 which is the same as the SR factor. Gaussian kernels are of size 13 13 with maximum variance 9. The down-/up-sampling scale factor during curriculum learning is decreasing from 3.5 to 1.2. In Ltotal, we set λ1 = 1, λ2 = 0.001. |