SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution
Authors: Wenlong Zhang, Xiaohui Li, Xiangyu Chen, Xiaoyun Zhang, Yu Qiao, Xiao-Ming Wu, Chao Dong
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Under SEAL, we benchmark existing real-SR methods, obtain new observations and insights into their performance, and develop a new strong baseline. In this work, we establish a systematic evaluation framework for real-SR, namely SEAL, which assesses relative, distributed, and overall performance rather than relying solely on absolute, average, and misleading evaluation strategy commonly used in current evaluation methods. Section 5 is titled EXPERIMENTS and contains numerous tables and figures showing empirical results. |
| Researcher Affiliation | Academia | 1The Hong Kong Polytechnic University, 2Shanghai AI Laboratory 3Shanghai Jiao Tong University 4University of Macau 5Shenzhen Institute of Advanced Technology, CAS |
| Pseudocode | Yes | Algorithm 1 Image degradation clustering |
| Open Source Code | Yes | The source code is available at https://github.com/XPixelGroup/SEAL |
| Open Datasets | Yes | We take Set14 (Zeyde et al., 2010) and DIV2K val (Lim et al., 2017) to construct the test sets for systematic evaluation, denoted as Set14-SE and DIV2K val-SE, respectively. We use the 100 representative degradation parameters to synthesize 100 training datasets based on DIV2K. |
| Dataset Splits | Yes | We take Set14 (Zeyde et al., 2010) and DIV2K val (Lim et al., 2017) to construct the test sets for systematic evaluation, denoted as Set14-SE and DIV2K val-SE, respectively. we randomly add degradations to images in the DIV2K (Agustsson & Timofte, 2017) validation set to construct a single real-DIV2K val set. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments, such as GPU or CPU models, or cloud resources. |
| Software Dependencies | No | The paper mentions the use of the Adam optimizer but does not specify versions for any programming languages, libraries, or other software dependencies required to reproduce the experiments. |
| Experiment Setup | Yes | The models within the model zoo are initially pre-trained under the real-SR setting. Subsequently, they undergo a fine-tuning process consisting of a total of 2 × 10^5 iterations. The Adam (Kingma & Ba, 2014) optimizer with β1 = 0.9 and β2 = 0.99 is used for training. The initial learning rate is 2 × 10^-4. We adopt L1 loss to optimize the networks. |