VQCNIR: Clearer Night Image Restoration with Vector-Quantized Codebook
Authors: Wenbin Zou, Hongxia Gao, Tian Ye, Liang Chen, Weipeng Yang, Shasha Huang, Hongsheng Chen, Sixiang Chen
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate the remarkable benefits of VQCNIR in enhancing image quality under low-light conditions, showcasing its state-of-the-art performance on both synthetic and real-world datasets. |
| Researcher Affiliation | Academia | 1The School of Automation Science and Engineering, South China University of Technology, Guangzhou 2Research Center for Brain-Computer Interface, Pazhou Laboratory, Guangzhou 3The Hong Kong University of Science and Technology, Guangzhou 4College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/Alex Zou14/VQCNIR. |
| Open Datasets | Yes | We train our VQCNIR network on the LOL-Blur dataset (2022) |
| Dataset Splits | No | We train our VQCNIR network on the LOL-Blur dataset (2022), which consists of 170 sequences (10,200 pairs) of training data and 30 sequences (1,800 pairs) of test data. |
| Hardware Specification | Yes | All experiments are performed on a PC equipped with Intel Core i7-13700K CPU, 32G RAM, and the Nvidia RTX 3090 GPU with CUDA 11.2. |
| Software Dependencies | Yes | CUDA 11.2 |
| Experiment Setup | Yes | We train our network using Adam (2014) optimizer with β1=0.9, β2=0.99 for a total of 500k iterations. The mini-batch size is set to 8. The initial learning rate is set to 1 10 4 and adopts the Multi Step LR to adjust the learning rate progressively. We empirically set λpix, λca, λper, and λadv to {1, 1, 1, 0.1}. |