Setup

Create and activate a new Python 3.10 virtual environment, for example via

conda create -n driftrec python=3.10
conda activate driftrec

Install the requirements via:

pip install -r requirements.txt

and to ensure that PyTorch uses the CUDA version you have on your system, you can here optionally pass an --extra-index-url parameter such as --extra-index-url https://download.pytorch.org/whl/cu118, see https://pytorch.org/.

Dataset preparation

• Our scripts expect each dataset to have a train/, valid/ and test/ subfolder, each containing image files with no additional subfolders or labels.

• For CelebA-HQ 256, please download the CelebA-HQ dataset and resize it to 256x256. Then you can generate the same train/validation/test split as we used with:

original_root = "<your_celeba_hq_256_folder>"
original_image_glob = original_root + "*.jpg"
imgfiles = np.array(list(sorted(glob.glob(original_image_glob))))
N = len(imgfiles)
print(f"Found {N} image files.")  # for CelebA-HQ, should be 30,000.

seed = 102405
np.random.seed(seed)
np.random.shuffle(imgfiles)

idxs = np.arange(N)
id_train = int(len(idxs) * 0.8)              # Train 80%
id_valid = int(len(idxs) * (0.8 + 0.05))     # Valid 5%, Test 15%
train, valid, test = np.split(idxs, (id_train, id_valid))
print(len(train), len(valid), len(test))

datasets = { "train": imgfiles[train], "valid": imgfiles[valid], "test": imgfiles[test] }

and copy/move all files in each respective subset to a matching subfolder (train/, valid/ and test/).

• For D2KF2K, please download the DIV2K_HR and Flickr2K datasets.
  • For Flickr2K, we generated the train/validation/test split with exactly the same script and random split as for CelebA-HQ above.
  • For DIV2K_HR, the original images for the test set are not available, so we used the images assigned by the authors as the 'validation set' as the test set instead. As a validation set, we randomly chose a small subset of the original training data (also removing them from the training set). These files are listed in div2k_own_validation_files.txt.
  • We then respectively merged the train/, valid/ and test/ subfolders from both generated datasets to get the overall D2KF2K dataset.

Training

Our training runs, as presented in our journal submission, can be performed by the following commands:

• Best D2KF2K run (OUVE SDE):

python train.py --gpus 1 --D-jpeg 0 100 --max_epochs 300 --model ScoreModel \
    --lr 0.0001 --batch_size 8 --optimizer adamw --loss_type mse \
    --t-eps 0.01 --sde ouve --sigma-min 0.01 --sigma-max 1 --gamma 1 --sde-n 100 \
    --backbone ncsnpp --ema_decay 0.999 \
    --data_module imagefolder --data_dir <your_d2kf2k_folder> \
    --random-resized-crop 256

• Best CelebA-HQ256 run (CosVE SDE):

python train.py --gpus 1 --D-jpeg 0 100 --max_epochs 300 --model ScoreModel \
    --lr 0.0001 --batch_size 8 --optimizer adamw --loss_type mse \
    --t-eps 0.01 --sde cosve --sigma-min 0.01 --sigma-max 1 --gamma 1 --sde-n 100 \
    --backbone ncsnpp --ema_decay 0.999 \
    --data_module imagefolder --data_dir <your_celebahq256_folder>

Regression baselines

• Regression Baseline training for D2KF2K:

python train.py --gpus 1 --D-jpeg 0 100 --max_epochs 3000 \
    --model DiscriminativeModel --discriminatively --discriminative_mode direct \
    --lr 0.0001 --batch_size 8 --optimizer adamw --loss_type mse \
    --backbone ncsnpp --ema_decay 0.999 \
    --data_module imagefolder --data_dir <your_image_dataset_folder> \
    --random-resized-crop 256

• Regression Baseline training for CelebA-HQ256:

python train.py --gpus 1 --D-jpeg 0 100 --max_epochs 300 \
    --model DiscriminativeModel --discriminatively --discriminative_mode direct \
    --lr 0.0001 --batch_size 8 --optimizer adamw --loss_type mse \
    --backbone ncsnpp --ema_decay 0.999 \
    --data_module imagefolder --data_dir <your_celebahq256_folder>

where the D2KF2K trainings run for 3000 epochs since there are much fewer images in the dataset (and the model will only see a single random resized crop for each one in each epoch).

Sampling / JPEG Artifact Removal

For enhancement, we used the provided enhance_folder.py script. For the default Euler-Maruyama sampling with 100 steps, as described in our manuscript, you can use:

python enhance_folder.py --ckpt <path_to_ckpt_file> --indir <path_to_corrupted_jpeg_folder> --outdir <path_to_output_folder> --ema --N 100 --batch_size 1

where the --batch_size can be increased for parallel processing, but only if all images are of the same resolution (e.g. 256x256).

Outputs will be different each time due to our method being stochastic! If you want reproducibility, feel free to set a seed with torch.random.manual_seed.

Evaluation

We have provided the following scripts for evaluation:

• avg_samples.py: Averages multiple image samples from a set of enhanced image folders into a single estimate.
• color_correct.py: Applies a per-channel global color correction to all images in a folder, see the "Limitations" section.
• calc_dist_feats.py: Calculates KID and FID between a folder containing image estimates and a ground-truth folder.
• eval_all_metrics.py: Evaluates per-image PSNR/PSNRB/SSIM/LPIPS between a folder containing image estimates and a ground-truth folder. Stores results in a .pkl pickled DataFrame.
• eval_blockiness.py: Calculates the blockiness measure (BEF) for a folder of images. Stores results in a .pkl pickled DataFrame.
• eval_resnet50_acc.py: Evaluates classifier accuracy using the pretrained ResNet-50 from torchvision. Written for ImageNet256 evaluation.