Pyramid Diffusion Models for Low-light Image Enhancement
Authors: Dewei Zhou, Zongxin Yang, Yi Yang
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on popular benchmarks show that Py Diff achieves superior performance and efficiency. Moreover, Py Diff can generalize well to unseen noise and illumination distributions. Code and supplementary materials are available at https://github.com/limuloo/Py DIff.git. |
| Researcher Affiliation | Academia | Dewei Zhou , Zongxin Yang , Yi Yang Re LER, CCAI, Zhejiang University {zdw1999, yangzongxin, yangyics}@zju.edu.cn |
| Pseudocode | Yes | Algorithm 1 Training" and "Algorithm 2 Sampling" are presented on page 5. |
| Open Source Code | Yes | Code and supplementary materials are available at https://github.com/limuloo/Py DIff.git. |
| Open Datasets | Yes | We conduct experiments on LOL [Wei et al., 2018] and LOLV2 [Yang et al., 2021] datasets. |
| Dataset Splits | No | The paper mentions a test set for LOLV2 REAL PART but does not specify a validation dataset split for hyperparameter tuning or early stopping. |
| Hardware Specification | Yes | We complete training on two NVIDIA Ge Force RTX 3090s. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not provide specific version numbers for software dependencies such as deep learning frameworks or libraries. |
| Experiment Setup | Yes | We set the patch size to 192 × 288 and the batch size to 16. We use the Adam optimizer with an initial learning rate of 1 × 10−4 for 320k iterations and halve the learning rate at 50k, 75k, 100k, 150k, and 200k. The optimizer does not use weight decay. |