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..
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 | Venue PDF | 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}. |