Low-Light Face Super-resolution via Illumination, Structure, and Texture Associated Representation
Authors: Chenyang Wang, Junjun Jiang, Kui Jiang, Xianming Liu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that the proposed joint optimization framework achieves significant improvements in reconstruction quality and perceptual quality over existing two-stage sequential solutions. |
| Researcher Affiliation | Academia | Chenyang Wang, Junjun Jiang*, Kui Jiang, Xianming Liu School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China {wangchy02, jiangjunjun, jiangkui, csxm}@hit.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/wcy-cs/IC-FSRDENet. |
| Open Datasets | Yes | We conduct experiments on commonly-used high quality face dataset Celeb AMask-HQ (Yu et al. 2018). |
| Dataset Splits | Yes | From it, we randomly choose 3050 face images for training, and 600 for validation, and another 300 for testing. |
| Hardware Specification | Yes | The experiments are implemented on Py Torch with one NVIDIA 3090 GPU. |
| Software Dependencies | No | The experiments are implemented on Py Torch with one NVIDIA 3090 GPU. This only mentions “Py Torch” without a specific version number and does not list other software dependencies with versions. |
| Experiment Setup | Yes | In IC-FSRNet, L is set as 14. IC-FSRNet and DENet are trained successively and individually. The optimizer is Adam with β1=0.9, β2=0.99, and ϵ=1e-8. The learning rate is 1e-4 for both two networks. Towards the DENet, the backbone is UNet and other settings follow (Saharia et al. 2021). |