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Deep-Burst-SR

Official implementation of Deep Burst Super-Resolution

News:

• The code for our ICCV 2021 oral paper "Deep Reparametrization of Multi-Frame Super-Resolution and Denoising" is now available at goutamgmb/deep-rep
• The complete training code is available now!

Publication: Deep Burst Super-Resolution. Goutam Bhat, Martin Danelljan, Luc Van Gool, and Radu Timofte. CVPR 2021 [Arxiv]

Overview

While single-image super-resolution (SISR) has attracted substantial interest in recent years, the proposed approaches are limited to learning image priors in order to add high frequency details. In contrast, multi-frame superresolution (MFSR) offers the possibility of reconstructing rich details by combining signal information from multiple shifted images. This key advantage, along with the increasing popularity of burst photography, have made MFSR an important problem for real-world applications. We propose a novel architecture for the burst superresolution task. Our network takes multiple noisy RAW images as input, and generates a denoised, super-resolved RGB image as output. This is achieved by explicitly aligning deep embeddings of the input frames using pixel-wise optical flow. The information from all frames are then adaptively merged using an attention-based fusion module. In order to enable training and evaluation on real-world data, we additionally introduce the BurstSR dataset, consisting of smartphone bursts and high-resolution DSLR ground-truth.

[Comparison of our Deep Burst SR apporach with Single Image baseline for 4x super-resolution of RAW burst captured from Samsung Galaxy S8]

Table of Contents

• Installation
• Toolkit Overview
• Datasets
• Evaluation
• Model Zoo
• Training
• Acknowledgement

Installation

Clone the Git repository.

git clone https://github.com/goutamgmb/deep-burst-sr.git

Install dependencies

Run the installation script to install all the dependencies. You need to provide the conda install path (e.g. ~/anaconda3) and the name for the created conda environment (here env-dbsr).

bash install.sh conda_install_path env-dbsr

This script will also download the default DBSR networks and create default environment settings.

Update environment settings

The environment setting file admin/local.py contains the paths for pre-trained networks, datasets etc. Update the paths in local.py according to your local environment.

Toolkit Overview

The toolkit consists of the following sub-modules.

• actors: Contains the actor classes for different trainings. The actor class is responsible for passing the input data through the network can calculating losses.
• admin: Includes functions for loading networks, tensorboard etc. and also contains environment settings.
• data: Contains functions for generating synthetic bursts, camera pipeline, processing data (e.g. loading images, data augmentations).
• data_specs: Information about train/val splits of different datasets.
• dataset: Contains integration of datasets such as BurstSR, SyntheticBurst, ZurichRAW2RGB.
• evaluation: Scripts to run and evaluate models on standard datasets.
• external: External dependencies, e.g. PWCNet.
• models: Contains different layers and network definitions.
• train_settings: Default training settings for different models.
• trainers: The main class which runs the training.
• util_scripts: Util scripts to e.g. download datasets.
• utils: General utility functions for e.g. plotting, data type conversions, loading networks.

Datasets

The toolkit provides integration for following datasets which can be used to train/evaluate the models.

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 from here. Set the burstsr_dir variable in admin/local.py to point to the unpacked BurstSR dataset directory.

Evaluation

You can run the trained model on RAW bursts to generate HR RGB images and compute the quality of predictions using the evaluation module.

Note: Please prepare the necessary datasets as explained in Datasets section before running the models.

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.

Model Zoo

Here, we provide pre-trained network weights and report their performance.

Note: The models have been retrained using the cleaned up code, and thus can have small performance differences compared to the models used for the paper.

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.

Training

You can train the models using the run_training.py script. Please download and set up the necessary datasets as described in Datasets section, before starting the trainings. You will also need a pre-trained PWC-Net model to start the trainings. The model is automatically downloaded from the install.sh script. You can also download it manually using

gdown https://drive.google.com/uc\?id\=1s11Ud1UMipk2AbZZAypLPRpnXOS9Y1KO -O pretrained_networks/pwcnet-network-default.pth

You can train a DBSR model on the SyntheticBurst dataset using default parameter settings using the following command

python run_training.py dbsr default_synthetic

In order to fine-tune DBSR model trained on SyntheticBurst dataset on the real-world BurstSR dataset, use the following command

python run_training.py dbsr default_realworld

Acknowledgement

The toolkit uses code from the following projects:

• Forward and inverse camera pipeline code from timothybrooks/unprocessing.
• PWC-Net code from sniklaus/pytorch-pwc
• PWC-Net pre-trained models from NVlabs/PWC-Net
• MS-SSIM code from jorge-pessoa/pytorch-msssim.
• Core code for the toolkit (e.g. saving and loading models) from visionml/pytracking