CURL: Neural Curve Layers for Global Image Enhancement (ICPR 2020)

Sean Moran, Steven McDonagh, Greg Slabaugh

Huawei Noah's Ark Lab

[Paper] [Supplementary] [Video] [Poster] [Slides]

Repository links for the paper CURL: Neural Curve Layers for Global Image Enhancement. Here you will find a link to the code and information of the datasets. Please raise a Github issue if you need assistance of have any questions on the research.

CURL for RGB images

• CURL_for_RGB_images.zip contains the TED model for RGB images - courtsey of Github user hermosayhl

Pretrained models weights

• CURL_for_RGB_images.zip contains pre-trained weights for RGB images - courtsey of Github user hermosayhl

Bibtex

If you do use ideas from the paper in your research please kindly consider citing as below:

@misc{moran2019curl,
    title={CURL: Neural Curve Layers for Global Image Enhancement},
    author={Sean Moran and Steven McDonagh and Gregory Slabaugh},
    year={2019},
    eprint={1911.13175},
    archivePrefix={arXiv},
    primaryClass={eess.IV}
}

Datasets

• Samsung S7 (110 images, RAW, RGB pairs): this dataset can be downloaded here. The validation and testing images are listed below, the remaining images serve as our training dataset. For all results in the paper we use random crops of patch size 512x512 pixels during training.

  • Validation Dataset Images

    • S7-ISP-Dataset-20161110_125321
    • S7-ISP-Dataset-20161109_131627
    • S7-ISP-Dataset-20161109_225318
    • S7-ISP-Dataset-20161110_124727
    • S7-ISP-Dataset-20161109_130903
    • S7-ISP-Dataset-20161109_222408
    • S7-ISP-Dataset-20161107_234316
    • S7-ISP-Dataset-20161109_132214
    • S7-ISP-Dataset-20161109_161410
    • S7-ISP-Dataset-20161109_140043

  • Test Dataset Images

    • S7-ISP-Dataset-20161110_130812
    • S7-ISP-Dataset-20161110_120803
    • S7-ISP-Dataset-20161109_224347
    • S7-ISP-Dataset-20161109_155348
    • S7-ISP-Dataset-20161110_122918
    • S7-ISP-Dataset-20161109_183259
    • S7-ISP-Dataset-20161109_184304
    • S7-ISP-Dataset-20161109_131033
    • S7-ISP-Dataset-20161110_130117
    • S7-ISP-Dataset-20161109_134017

• Adobe-DPE (5000 images, RGB, RGB pairs): this dataset can be downloaded here. After downloading this dataset you will need to use Lightroom to pre-process the images according to the procedure outlined in the DeepPhotoEnhancer (DPE) paper. Please see the issue here for instructions. Artist C retouching is used as the groundtruth/target. Feel free to raise a Gitlab issue if you need assistance with this (or indeed the Adobe-UPE dataset below). You can also find the training, validation and testing dataset splits for Adobe-DPE in the following file.

• Adobe-UPE (5000 images, RGB, RGB pairs): this dataset can be downloaded here. As above, you will need to use Lightroom to pre-process the images according to the procedure outlined in the Underexposed Photo Enhancement Using Deep Illumination Estimation (DeepUPE) paper and detailed in the issue here. Artist C retouching is used as the groundtruth/target. You can find the test images for the Adobe-UPE dataset at this link.

License

BSD-3-Clause License

Contributions

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.

If you plan to contribute new features, utility functions or extensions to the core, please first open an issue and discuss the feature with us. Sending a PR without discussion might end up resulting in a rejected PR, because we might be taking the core in a different direction than you might be aware of.