EEMEFN: Low-Light Image Enhancement via Edge-Enhanced Multi-Exposure Fusion Network
Authors: Minfeng Zhu, Pingbo Pan, Wei Chen, Yi Yang13106-13113
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the See-in-the-Dark dataset indicate that our EEMEFN approach achieves state-of-the-art performance. |
| Researcher Affiliation | Collaboration | Minfeng Zhu,1,2 Pingbo Pan,2,3 Wei Chen,1 Yi Yang2 1State Key Lab of CAD&CG, Zhejiang University 2The Re LER Lab, University of Technology Sydney, 3Baidu Research |
| Pseudocode | No | The paper describes the model architecture and processes but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We evaluate the EEMEFN model quantitatively and qualitatively on the See-in-the-Dark dataset (Chen et al. 2018). The See-in-the-Dark dataset consists of two image sets: Sony set and Fuji set. |
| Dataset Splits | Yes | We evaluate the EEMEFN model quantitatively and qualitatively on the See-in-the-Dark dataset (Chen et al. 2018). Following Chen et al. (2018), we preprocess all raw images by subtracting the black level. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | We implemented our method based on the Tensorflow framework and the Paddlepaddle framework. The paper mentions software frameworks but does not provide specific version numbers for them. |
| Experiment Setup | Yes | We train EEMEFN for 5000 epochs using ADAM (Kinga and Adam 2015) optimizer with an initial learning rate of 10 4, which is decreased to 5 10 5 after 2500 epochs and 10 5 after 3500 epochs. |