Sean Moran, Pierre Marza, Steven McDonagh, Sarah Parisot, Greg Slabaugh

Huawei Noah's Ark Lab

17th July 2020: Code is located here.

requirements.txt contains the Python packages used by the code.

@InProceedings{Moran_2020_CVPR,
author = {Moran, Sean and Marza, Pierre and McDonagh, Steven and Parisot, Sarah and Slabaugh, Gregory},
title = {DeepLPF: Deep Local Parametric Filters for Image Enhancement},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}

8th February 2021: Contact Sean Moran if you wish to download the Adobe5K pre-processed dataset (i.e. Adobe-DPE) according to the pre-processing procedure outline in the DeepPhotoEnhancer paper.

• 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. The splits can also be found the the Adobe5K_DPE directory in this repository.

• 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.

BSD-3-Clause License

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.