Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method
Authors: Tao Wang, Kaihao Zhang, Tianrun Shen, Wenhan Luo, Bjorn Stenger, Tong Lu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct systematic benchmarking studies and provide a comparison of current LLIE algorithms. As a second contribution, we introduce LLFormer, a transformer-based low-light enhancement method. Extensive experiments on the new dataset and existing public datasets show that LLFormer outperforms state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Tao Wang1, Kaihao Zhang2, Tianrun Shen1, Wenhan Luo3*, Bjorn Stenger4, Tong Lu1* 1State Key Lab for Novel Software Technology, Nanjing University, China 2 Australian National University, Australia 3 Shenzhen Campus of Sun Yat-sen University, China 4 Rakuten Institute of Technology, Japan |
| Pseudocode | No | The paper includes architectural diagrams and mathematical formulations, but no explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code and pre-trained models are available at https://github.com/Tao Wangzj/LLFormer. |
| Open Datasets | Yes | To build this dataset of image pairs, we use normal-light 4K and 8K images from public data (Zhang et al. 2021). These UHD images were crawled from the web and captured by various devices. |
| Dataset Splits | Yes | UHD-LOL includes two subsets, UHD-LOL4K and UHD-LOL8K, containing 4K and 8K-resolution images, respectively. The UHD-LOL4K subset contains 8, 099 image pairs, 5, 999 for training and 2, 100 for testing. The subset of UHD-LOL8K includes 2, 966 image pairs, 2, 029 for training and 937 for testing. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not specify software dependencies (e.g., programming languages, libraries, frameworks) with version numbers. |
| Experiment Setup | Yes | The LLFormer is trained on 128 128 patches with a batch size of 12. For data augmentation, we adopt horizontal and vertical flips. We use the Adam optimizer with an initial learning rate of 10 4 and decrease it to 10 6 using cosine annealing. The numbers of encoder blocks in the LLFormer from stage 1 to stage 4 are {2, 4, 8, 16}, and the number of attention heads in A-MSA are {1, 2, 4, 8}. The numbers corresponding to decoders from stage 1 to 3 are {2, 4, 8} and {1, 2, 4}. |