The RGB images from the training split of the Zurich RAW to RGB mapping dataset can be used to generate synthetic bursts for training using the SyntheticBurstProcessing class in data/processing.py.

Preparation: Download the Zurich RAW to RGB canon set from here and unpack the zip folder. Set the zurichraw2rgb_dir variable in admin/local.py to point to the unpacked dataset directory.

The pre-generated synthetic validation set used for evaluating the models. The dataset contains 300 synthetic bursts, each containing 14 RAW images. The synthetic bursts are generated from the RGB images from the test split of the Zurich RAW to RGB mapping dataset. The dataset can be loaded using SyntheticBurstVal class in dataset/synthetic_burst_val_set.py file.

Preparation: Downloaded the dataset here and unpack the zip file. Set the synburstval_dir variable in admin/local.py to point to the unpacked dataset directory.

The BurstSR dataset containing RAW bursts captured from Samsung Galaxy S8 and corresponding HR ground truths captured using a DSLR camera. This is the pre-processed version of the dataset that contains roughly aligned crops from the original images. The dataset can be loaded using BurstSRDataset class in dataset/burstsr_dataset.py file. Please check the DBSR paper for more details.

Preparation: The dataset has been split into 10 parts and can be downloaded and unpacked using the util_scripts/download_burstsr_dataset.py script. Set the burstsr_dir variable in admin/local.py to point to the unpacked BurstSR dataset directory.

The BurstSR dataset containing RAW bursts captured from Samsung Galaxy S8 and corresponding HR ground truths captured using a DSLR camera. This is the raw version of the dataset containing the full burst images in dng format.

Preparation: The dataset can be downloaded and unpacked using the util_scripts/download_raw_burstsr_data.py script.

You can evaluate the models on SyntheticBurst validation set using evaluation/synburst package. First create an experiment setting in evaluation/synburst/experiments containing the list of models to evaluate. You can start with the provided setting dbsr_default.py as a reference. Please refer to network_param.py for examples on how to specify a model for evaluation.

Save network predictions

You can save the predictions of a model on bursts from SyntheticBurst dataset by running

python evaluation/synburst/save_results.py EXPERIMENT_NAME

Here, EXPERIMENT_NAME is the name of the experiment setting you want to use (e.g. dbsr_default). The script will save the predictions of the model in the directory pointed by the save_data_path variable in admin/local.py.

Note The network predictions are saved in linear sensor color space (i.e. color space of input RAW burst), as 16 bit pngs.

Compute performance metrics

You can obtain the standard performance metrics (e.g. PSNR, MS-SSIM, LPIPS) using the compute_score.py script

python evaluation/synburst/compute_score.py EXPERIMENT_NAME

Here, EXPERIMENT_NAME is the name of the experiment setting you want to use (e.g. dbsr_default). The script will run the models to generate the predictions and compute the scores. In case you want to compute performance metrics for results saved using save_results.py, you can run compute_score.py with additonal --load_saved argument.

python evaluation/synburst/compute_score.py EXPERIMENT_NAME --load_saved

In this case, the script will load pre-saved predictions whenever available. If saved predictions are not available, it will run the model to first generate the predictions and then compute the scores.

Qualitative comparison

You can perform qualitative analysis of the model by visualizing the saved network predictions, along with ground truth, in sRGB format using the visualize_results.py script.

python evaluation/synburst/visualize_results.py EXPERIMENT_NAME

Here, EXPERIMENT_NAME is the name of the experiment setting containing the list of models you want to use (e.g. dbsr_default). The script will display the predictions of each model in sRGB format, along with the ground truth. You can toggle between images, zoom in on particular image regions using the UI. See visualize_results.py for details.

Note: You need to first save the network predictions using save_results.py script, before you can visualize them using visualize_results.py.

You can evaluate the models on BurstSR validation set using evaluation/burstsr package. First create an experiment setting in evaluation/burstsr/experiments containing the list of models to evaluate. You can start with the provided setting dbsr_default.py as a reference. Please refer to network_param.py for examples on how to specify a model for evaluation.

Save network predictions

You can save the predictions of a model on bursts from BurstSR val dataset by running

python evaluation/burstsr/save_results.py EXPERIMENT_NAME

Here, EXPERIMENT_NAME is the name of the experiment setting you want to use (e.g. dbsr_default). The script will save the predictions of the model in the directory pointed by the save_data_path variable in admin/local.py.

Note The network predictions are saved in linear sensor color space (i.e. color space of input RAW burst), as 16 bit pngs.

Compute performance metrics

You can obtain the standard performance metrics (e.g. PSNR, MS-SSIM, LPIPS) after spatial and color alignment (see paper for details) using the compute_score.py script

python evaluation/burstsr/compute_score.py EXPERIMENT_NAME

Here, EXPERIMENT_NAME is the name of the experiment setting you want to use (e.g. dbsr_default). The script will run the models to generate the predictions and compute the scores. In case you want to compute performance metrics for results saved using save_results.py, you can run compute_score.py with additonal --load_saved argument.

python evaluation/burstsr/compute_score.py EXPERIMENT_NAME --load_saved

In this case, the script will load pre-saved predictions whenever available. If saved predictions are not available, it will run the model to first generate the predictions and then compute the scores.

Qualitative comparison

You can perform qualitative analysis of the model by visualizing the saved network predictions, along with ground truth, in sRGB format using the visualize_results.py script.

python evaluation/burstsr/visualize_results.py EXPERIMENT_NAME

Here, EXPERIMENT_NAME is the name of the experiment setting containing the list of models you want to use (e.g. dbsr_default). The script will display the predictions of each model in sRGB format, along with the ground truth. You can toggle between images, zoom in on particular image regions using the UI. See visualize_results.py for details.

Note: You need to first save the network predictions using save_results.py script, before you can visualize them using visualize_results.py.

The models are evaluated using all 14 burst images.

The models are evaluated using all 14 burst images. The metrics are computed after spatial and color alignment, as described in DBSR paper.