Interpretable Unsupervised Joint Denoising and Enhancement for Real-World low-light Scenarios
Authors: Li Huaqiu, HuXiaowan, Haoqian Wang
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of our method. Code will be available at https://github.com/huaqlili/ unsupervised-light-enhance-ICLR2025. [...] Extensive experiments on multiple real-world datasets demonstrate that our method achieves superior performance across several metrics compared to SOTA approaches. [...] We conducted tests on four benchmarks: LOLv1 Wei et al. (2018), LOLv2-real Yang et al. (2021), SICE Cai et al. (2018) and SIDD Abdelhamed et al. (2018). [...] The experimental results on the LOL dataset are presented in Tab. 1, where our model outperforms most of the compared unpaired and no-reference methods, achieving the highest scores across multiple metrics. [...] The experimental results on the SICE and SIDD datasets are shown in Tab. 2. [...] Table 3: Ablation study of the contribution of the three physical priors. [...] Table 4: Ablation study of the contribution of the denoising designs |
| Researcher Affiliation | Academia | Huaqiu Li, Xiaowan Hu, Haoqian Wang Tsinghua Shenzhen International Graduate School Tsinghua University EMAIL |
| Pseudocode | No | The paper describes methods using mathematical equations and architectural diagrams (e.g., Figure 2), but does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | Code will be available at https://github.com/huaqlili/ unsupervised-light-enhance-ICLR2025. |
| Open Datasets | Yes | We conducted tests on four benchmarks: LOLv1 Wei et al. (2018), LOLv2-real Yang et al. (2021), SICE Cai et al. (2018) and SIDD Abdelhamed et al. (2018). |
| Dataset Splits | No | Please refer to the supplementary materials for detailed information regarding the datasets, including the corresponding training and testing splits. |
| Hardware Specification | Yes | We consistently set the initial learning rate to 1 10 5 and conducted all experiments on an RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions implementing the method and training, but does not provide specific version numbers for software dependencies like Python, PyTorch, or other libraries used. |
| Experiment Setup | Yes | To ensure fairness, all experiments were terminated after 100 training epochs. We consistently set the initial learning rate to 1 10 5 and conducted all experiments on an RTX 3090 GPU. During training, images were randomly cropped into 256x256 patches, with pixel values normalized to the range of (0, 1), and a batch size of 1 was employed. [...] Therefore, during each iteration, we randomly sample enhancement factors within the range of (1.3, 1.7) to provide the model with a broader range of feature processing options. |