Abstract: Image enhancement is a subjective process whose targets vary with user preferences. In this paper, we propose a deep learning-based image enhancement method covering multiple tonal styles using only a single model dubbed StarEnhancer. It can transform an image from one tonal style to another, even if that style is unseen. With a simple one-time setting, users can customize the model to make the enhanced images more in line with their aesthetics. To make the method more practical, we propose a well-designed enhancer that can process a 4K-resolution image over 200 FPS but surpasses the contemporaneous single style image enhancement methods in terms of PSNR, SSIM, and LPIPS. Finally, our proposed enhancement method has good interactability, which allows the user to fine-tune the enhanced image using intuitive options.

We test the code on PyTorch 1.8.1 + CUDA 11.1 + cuDNN 8.0.5, and close versions also work fine.

• Install PyTorch and torchvision fom http://pytorch.org.
• Install other requirements:

pip install -r requirements.txt

We mainly train the model on RTX 2080Ti * 4, but a smaller mini batch size can also work.

You can generate your own dataset, or download the one we generate.

The final file path should be the same as the following:

┬─ save_model
│   ├─ stylish.pth.tar
│   └─ ... (model & embedding)
└─ data
    ├─ train
    │   ├─ 01-Experts-A
    │   │   ├─ a0001.jpg
    │   │   └─ ... (id.jpg)
    │   └─ ... (style folder)
    ├─ valid
    │   └─ ... (style folder)
    └─ test
        └─ ... (style folder)

Data and pretrained models are available on GoogleDrive or BaiduPan (jvyf).

1. Download raw data from MIT-Adobe FiveK Dataset.
2. Download the modified Lightroom database fivek.lrcat, and replace the original database with it.
3. Generate dataset in JPEG format with quality 100, which can refer to this issue.
4. Run generate_dataset.py in data folder to generate dataset.

Firstly, train the style encoder:

python train_stylish.py

Secondly, fetch the style embedding for each sample in the train set:

python fetch_embedding.py

Lastly, train the curve encoder and mapping network:

python train_enhancer.py

Just run:

python test.py

Testing LPIPS requires about 10 GB GPU memory, and if an OOM occurs, replace the following lines

lpips_val = loss_fn_alex(output * 2 - 1, target_img * 2 - 1).item()

with

lpips_val = 0

Due to licenses, we are unable to release part of the source code. This repository provides a pure python implementation for research use. There are some differences between the repository and the paper as follows:

1. The repository uses a ResNet-18 w/o BN as the curve encoder's backbone, and the paper uses a more lightweight model.
2. The paper uses CUDA to implement the color transform function, and the repository uses torch.gather to implement it.
3. The repository removes some tricks used in training lightweight models.

Overall, this repository can achieve higher performance, but will be slightly slower.

If you find this work useful for your research, please cite our paper:

@inproceedings{song2021starenhancer,
  title={StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement},
  author={Song, Yuda and Qian, Hui and Du, Xin},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={4126--4135},
  year={2021}
}