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RetinexMamba

这是"RetinexMamba:Retinex-based Mamba for Low-light Image Enhancement"的官方代码。论文请见{Arxiv}

摘要

In the field of low-light image enhancement, traditional Retinex methods and deep learning-based techniques such as Retinexformer each display unique advantages and limitations. Traditional Retinex methods, by simulating the human eye's perception of brightness and color, decompose images into illumination and reflection components. However, they often fail to adequately consider noise and detail loss introduced by low lighting. Retinexformer, while optimizing illumination estimation through deep learning, suffers from high computational complexity and requires multi-stage training. Addressing these challenges, this paper introduces the RetinexMamba architecture, which not only inherits the physical intuitiveness of traditional Retinex methods but also incorporates the deep learning framework of Retinexformer, further integrating the efficient computational capabilities of State Space Models (SSMs). RetinexMamba enhances image illumination and repairs damages during the enhancement process through innovative illumination estimators and damage repairers, while substituting the IG-MSA in Retinexformer with a Fused-Attention mechanism to enhance model interpretability. Experimental results on the LOL dataset demonstrate that RetinexMamba surpasses existing deep learning methods based on Retinex theory in both quantitative and qualitative metrics, proving its effectiveness and superiority in low-light image enhancement tasks.

1. 下载项目

请您运行下面的命令以确保您将我们的项目部署到本地

git clone https://github.com/YhuoyuH/RetinexMamba.git

2. 创建环境

注意，由于后续的"causal_conv1d"包仅存在于Linux系统上，因此请确保您所使用的操作环境是Linux

2.1 创建Conda环境

为了防止您和我们所使用的环境版本不一致的问题，我们建议您选择和我们相同的虚拟环境，您可直接安装我们已经为您打包好的环境，也可选择跟随我们的教程一步步安装。

2.1.1 直接使用压缩包

请将百度网盘的RetinexMamba.tar.zip压缩包下载下来，并利用下面的命令进行解压

cd /RetinexMamba.tar.zip所在的文件夹/
tar -xzf RetinexMamba.tar.zip -C /你的anaconda的env文件路径/

若您采取这条命令，则不需要进行 "Create Environment"中的后续操作，直接跳到第二部分即可

2.1.2 利用包安装

conda create -n RetinexMamba python=3.8
conda activate RetinexMamba

2.2 安装依赖项

pip install torch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu117
pip install packaging
pip install timm==0.4.12
pip install pytest chardet yacs termcolor
pip install submitit tensorboardX
pip install triton==2.0.0
pip install causal_conv1d==1.0.0
pip install mamba_ssm==1.0.1
pip install scikit-learn matplotlib thop h5py SimpleITK scikit-image medpy
pip install opencv-python joblib natsort tqdm tensorboard
pip install einops gdown addict future lmdb numpy pyyaml requests scipy yapf lpips

注意，您可能在安装causal_conv1d以及mamba_ssm会出现网络异常而导致一直卡在Building wheel for mamba ssm(setup.py)的问题。因此请您下载百度网盘中的.whl文件拷贝在本地，并运行下面的命令进行手动安装

pip install 路径/文件名.whl

2.3 安装BasicSR

cd /RetinexMamba/
python setup.py develop --no_cuda_ext

3. 准备数据集

如果data为空，请从百度网盘中下载数据集，并将data文件放置在RetinexMamba的文件夹下。

最终放置的结果如下：

|--RetinexMamba  	
|  	 |--data   
|    |    |--LOLv1
|    |    |    |--Train
|    |    |    |    |--input
|    |    |    |    |--target
|    |    |    |--Test
|    |    |    |    |--input
|    |    |    |    |--target
|    |    |--LOLv2
|    |    |    |--Real_captured
|    |    |    |    |--Train
|    |    |    |    |    |--Low
|    |    |    |    |    |--Normal
|    |    |    |    |--Test
|    |    |    |    |    |--Low
|    |    |    |    |    |--Normal
|    |    |    |--Synthetic
|    |    |    |    |--Train
|    |    |    |    |    |--Low
|    |    |    |    |    |--Normal
|    |    |    |    |--Test
|    |    |    |    |    |--Low
|    |    |    |    |    |--Normal

4. 测试

请确保pretrained_weights文件夹下有我们预训练的权重，如果您的权重文件丢失，请从百度网盘下载。

4.1 测试峰值信噪比(PSNR)和结构相似度(SSIM)

# activate the environment
conda activate RetinexMamba

# LOL-v1
python3 Enhancement/test_from_dataset.py --opt Options/RetinexMamba_LOL_v1.yml --weights pretrained_weights/LOL_v1.pth --dataset LOL_v1

# LOL-v2-real
python3 Enhancement/test_from_dataset.py --opt Options/RetinexMamba_LOL_v2_real.yml --weights pretrained_weights/LOL_v2_real.pth --dataset LOL_v2_real

# LOL-v2-synthetic
python3 Enhancement/test_from_dataset.py --opt Options/RetinexMamba_LOL_v2_synthetic.yml --weights pretrained_weights/LOL_v2_synthetic.pth --dataset LOL_v2_synthetic

4.2 测试均方根误差(RMSE)

请运行位于 Enhancement 目录中的 RMSE.py 文件，并确保第54行的代码：

ave_psnr, ave_ssim, ave_rmse = evaluate_raindrop('Your result dir', 'data GT')

其中的Your result dir以及data GT被替换为了您在测试PSNR和SSIM时生成的result以及data/LOLV1 or LOLV2/../Test/GT

5. 模型参数以及浮点数评估

如果你想看模型的参数量，请将ReinexMamba_arch中的第11和第12行代码：

from .SS2D_arch import SS2D 
from .IFA_arch import IFA

改成

from .SS2D_arch import SS2D 
from IFA_arch import IFA 

随后直接运行ReinexMamba_arch即可

6. 训练

请确保您已完整完成环境配置并且可以正常的推理出参数和浮点数

# activate the enviroment
conda activate RetinexMamba

# LOL-v1
python3 basicsr/train.py --opt Options/RetinexMamba_LOL_v1.yml

# LOL-v2-real
python3 basicsr/train.py --opt Options/RetinexMamba_LOL_v2_real.yml

# LOL-v2-synthetic
python3 basicsr/train.py --opt Options/RetinexMamba_LOL_v2_synthetic.yml

7.致谢

我们感谢下列文章的作者以及他们为开源社区所做的贡献

@inproceedings{Retinexformer,
  title={Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement},
  author={Yuanhao Cai and Hao Bian and Jing Lin and Haoqian Wang and Radu Timofte and Yulun Zhang},
  booktitle={ICCV},
  year={2023},
  reposittory:https://github.com/caiyuanhao1998/Retinexformer        
}

@inproceedings{VM-Unet,
  title={VM-UNet: Vision Mamba UNet for Medical Image Segmentation},
  author={Jiacheng Ruan, Suncheng Xiang},
  year={2024}
}

该仓库基于Retinexformer (ICCV 2023).