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README.md

DIFAR: Deep Image Formation and Retouching (Pre-print, paper under review)

Sean Moran, Greg Slabaugh

Huawei Noah's Ark Lab

[Paper]

Repository for the paper DIFAR: Deep Image Formation and Retouching. Here you will find the code, pre-trained models, information of the datasets, and information on the training procedure for our models. Please raise a Github issue if you need assistance of have any questions on the research.

Bibtex

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

@misc{moran2019difar,
    title={DIFAR: Deep Image Formation and Retouching},
    author={Sean Moran 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.

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