DPED

DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks

<br/> <img src="https://aiff22.github.io/assets/img/teaser_git.jpg"/> <br/>

1. Overview [Paper] [Project webpage] [Enhancing RAW photos] [Rendering Bokeh Effect]

The provided code implements the paper that presents an end-to-end deep learning approach for translating ordinary photos from smartphones into DSLR-quality images. The learned model can be applied to photos of arbitrary resolution, while the methodology itself is generalized to any type of digital camera. More visual results can be found here.

2. Prerequisites

• Python + Pillow, scipy, numpy, imageio packages
• TensorFlow 1.x / 2.x + CUDA CuDNN
• Nvidia GPU

3. First steps

• Download the pre-trained VGG-19 model and put it into vgg_pretrained/ folder
• Download DPED dataset (patches for CNN training) and extract it into dped/ folder.
  _{This folder should contain three subolders: sony/, iphone/ and blackberry/}

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4. Train the model

python train_model.py model=<model>

Obligatory parameters:

model: iphone, blackberry or sony

Optional parameters and their default values:

batch_size: 50   -   batch size [smaller values can lead to unstable training] <br/> train_size: 30000   -   the number of training patches randomly loaded each eval_step iterations <br/> eval_step: 1000   -   each eval_step iterations the model is saved and the training data is reloaded <br/> num_train_iters: 20000   -   the number of training iterations <br/> learning_rate: 5e-4   -   learning rate <br/> w_content: 10   -   the weight of the content loss <br/> w_color: 0.5   -   the weight of the color loss <br/> w_texture: 1   -   the weight of the texture [adversarial] loss <br/> w_tv: 2000   -   the weight of the total variation loss <br/> dped_dir: dped/   -   path to the folder with DPED dataset <br/> vgg_dir: vgg_pretrained/imagenet-vgg-verydeep-19.mat   -   path to the pre-trained VGG-19 network <br/>

Example:

python train_model.py model=iphone batch_size=50 dped_dir=dped/ w_color=0.7

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5. Test the provided pre-trained models

python test_model.py model=<model>

Obligatory parameters:

model: iphone_orig, blackberry_orig or sony_orig

Optional parameters:

test_subset: full,small   -   all 29 or only 5 test images will be processed <br/> resolution: orig,high,medium,small,tiny   -   the resolution of the test images [orig means original resolution]<br/> use_gpu: true,false   -   run models on GPU or CPU <br/> dped_dir: dped/   -   path to the folder with DPED dataset <br/>

Example:

python test_model.py model=iphone_orig test_subset=full resolution=orig use_gpu=true

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6. Test the obtained models

python test_model.py model=<model>

Obligatory parameters:

model: iphone, blackberry or sony

Optional parameters:

test_subset: full,small   -   all 29 or only 5 test images will be processed <br/> iteration: all or <number>   -   get visual results for all iterations or for the specific iteration,
               <number> must be a multiple of eval_step <br/> resolution: orig,high,medium,small,tiny   -   the resolution of the test images [orig means original resolution]<br/> use_gpu: true,false   -   run models on GPU or CPU <br/> dped_dir: dped/   -   path to the folder with DPED dataset <br/>

Example:

python test_model.py model=iphone iteration=13000 test_subset=full resolution=orig use_gpu=true

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7. Folder structure

dped/   -   the folder with the DPED dataset <br/> models/   -   logs and models that are saved during the training process <br/> models_orig/   -   the provided pre-trained models for iphone, sony and blackberry <br/> results/   -   visual results for small image patches that are saved while training <br/> vgg-pretrained/   -   the folder with the pre-trained VGG-19 network <br/> visual_results/   -   processed [enhanced] test images <br/>

load_dataset.py   -   python script that loads training data <br/> models.py   -   architecture of the image enhancement [resnet] and adversarial networks <br/> ssim.py   -   implementation of the ssim score <br/> train_model.py   -   implementation of the training procedure <br/> test_model.py   -   applying the pre-trained models to test images <br/> utils.py   -   auxiliary functions <br/> vgg.py   -   loading the pre-trained vgg-19 network <br/>

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8. Problems and errors

What if I get an error: "OOM when allocating tensor with shape [...]"?

   Your GPU does not have enough memory. If this happens during the training process:

• Decrease the size of the training batch [batch_size]. Note however that smaller values can lead to unstable training.

   If this happens while testing the models:

• Run the model on CPU (set the parameter use_gpu to false). Note that this can take up to 5 minutes per image. <br/>
• Use cropped images, set the parameter resolution to:

high   -   center crop of size 1680x1260 pixels <br/> medium   -   center crop of size 1366x1024 pixels <br/> small   -   center crop of size 1024x768 pixels <br/> tiny   -   center crop of size 800x600 pixels <br/>

   The less resolution is - the smaller part of the image will be processed

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9. Citation

@inproceedings{ignatov2017dslr,
  title={DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks},
  author={Ignatov, Andrey and Kobyshev, Nikolay and Timofte, Radu and Vanhoey, Kenneth and Van Gool, Luc},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={3277--3285},
  year={2017}
}

10. Any further questions?

Please contact Andrey Ignatov (andrey.ignatoff@gmail.com) for more information