Enhance Image as You Like with Unpaired Learning
Authors: Xiaopeng Sun, Muxingzi Li, Tianyu He, Lubin Fan
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our model achieves competitive visual and quantitative results on par with fully supervised methods on both noisy and clean datasets, while being 6 to 10 times lighter than state-of-the-art generative adversarial networks (GANs) approaches. and Table 1 shows a quantitative comparison between our method and the other baselines on the PSNR, SSIM and the NIQE [Mittal et al., 2012] metrics. and 4.2 Ablation Study We demonstrate the effectiveness of our choice of losses and the CCM block via ablation studies. |
| Researcher Affiliation | Collaboration | Xiaopeng Sun1 , Muxingzi Li2 , Tianyu He2 and Lubin Fan2 1Xidian University 2Alibaba Group xpsun@stu.xidian.edu.cn, {muxingzi.lmxz,timhe.hty}@alibaba-inc.com, lubinfan@gmail.com |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | Yes | Our code can be found at https://github.com/sxpro/Image Enhance c GAN. |
| Open Datasets | Yes | We assemble images from three different datasets [Wei et al., 2018; Bychkovsky et al., 2011; Loh and Chan, 2019] and ignore the paired information in each individual dataset if there is any, which leads to a larger and more diverse dataset that consists of 983 low-light and 5576 normal-light images. We follow the same practice of previous work[Yang et al., 2020] to use part of the LOL dataset[Wei et al., 2018] for training, and leaving the other part for testing. and Table 1: PSNR( ) \ SSIM( ) \ NIQE( ) metrics on the paired test set of datasets LOL [Wei et al., 2018] starting from image #690, and Five K [Bychkovsky et al., 2011]. |
| Dataset Splits | No | We follow the same practice of previous work[Yang et al., 2020] to use part of the LOL dataset[Wei et al., 2018] for training, and leaving the other part for testing. No explicit mention of validation split percentages or counts. |
| Hardware Specification | No | We implement our network with Py Torch on a Tesla GPU. This does not specify the exact model of the Tesla GPU. |
| Software Dependencies | No | We implement our network with Py Torch on a Tesla GPU. No version numbers for PyTorch or other software dependencies are provided. |
| Experiment Setup | Yes | We adopt Adam optimizer with default parameters and with learning rate set to 5 10 5. We set the loss weight λ in Eq. (6) to 0.9, and α in Eq. (7) to 0.05 in all the tests. |