Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
EEMEFN: Low-Light Image Enhancement via Edge-Enhanced Multi-Exposure Fusion Network
Authors: Minfeng Zhu, Pingbo Pan, Wei Chen, Yi Yang13106-13113
AAAI 2020 | Venue PDF | 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 Tensor๏ฌow 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. |