MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space
Authors: Jiangwei Weng, Zhiqiang Yan, Ying Tai, Jianjun Qian, Jian Yang, Jun Li
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that Mamba LLIE significantly outperforms state-of-the-art CNN and Transformer-based methods. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China 2School of Intelligence Science and Technology, Nanjing University, Suzhou, 215163, China |
| Pseudocode | No | The paper provides architectural diagrams (Figure 2) and describes modules in text but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at Project Page. |
| Open Datasets | Yes | We employ five paired low light image datasets for evaluation, including LOL-V2-real [59], LOL-V2-syn [59], SMID [4], SDSD-indoor [48] and SDSD-outdoor [48] datasets. |
| Dataset Splits | Yes | LOL-V2-real contains 689 low-normal light paired images for training and 100 pairs for testing; LOL-V2-syn includes 900 paired images for training and the 100 pairs for testing; Besides, SMID is composed of the 15763 short-long exposure paired images for training and the remaining images for testing; SDSD-indoor and SDSD-outdoor are all subsets of SDSD dataset (the static version), which extract the paired images from 62 and 116 pairs for training, and the left 6 and 10 pairs for testing. |
| Hardware Specification | Yes | We implement Mamba LLIE in Py Torch [40] on a server with the 4090GPUs. |
| Software Dependencies | No | The paper mentions 'Py Torch [40]' but does not specify a version number for it or any other software dependencies. |
| Experiment Setup | Yes | Random cropping the image pairs into 128 x 128 patches as training samples, data augmentation is performed on the training samples such as rotation and flipping. The batch size is 8. In terms of optimization procedure, Adam [24] is adopted as the optimizer with β1 = 0.9 and β2 = 0.999; The training iterations is set to 1.5 x 10^5. The initial e learning rate is set to 2 x 10^-4 and steadily decreased by by the cosine annealing scheme. The loss criterion is mean absolute error (MAE), thus peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) [52] is selected as the evaluation metrics for the paired datasets. |