Official implementation of Deep Burst Super-Resolution

Overview

Deep-Burst-SR

Official implementation of Deep Burst Super-Resolution

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.

dbsr overview figure [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

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.

  • 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.
  • 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.

Zurich RAW to RGB Canon set

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.

SyntheticBurst validation set

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.

BurstSR dataset (cropped)

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.

BurstSR dataset (full)

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.

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.

Evaluate on SyntheticBurst validation set

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.

Evaluate on BurstSR validation set

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.

SyntheticBurst models

The models are evaluated using all 14 burst images.

Model PSNR MS-SSIM LPIPS Links Notes
CVPR2021 39.09 0.945 0.084 - CVPR2021 results
dbsr_synthetic_default 39.17 0.946 0.081 model Official retrained model
BurstSR models

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

Model PSNR MS-SSIM LPIPS Links Notes
CVPR2021 47.76 0.984 0.030 - CVPR2021 results
dbsr_burstsr_default 47.70 0.984 0.029 model Official retrained model

Training

We are still waiting for approval from our project sponsors to release the training codes. Hopefully we can soon release it. Meanwhile, please free to contact us in case of any questions regarding training.

Acknowledgement

The toolkit uses code from the following projects:

Owner
Goutam Bhat
Goutam Bhat
Official implementation for (Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching, AAAI-2021)

Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching Official pytorch implementation of "Show, Attend and Distill: Kn

Clova AI Research 80 Dec 16, 2022
Official code release for ICCV 2021 paper SNARF: Differentiable Forward Skinning for Animating Non-rigid Neural Implicit Shapes.

Official code release for ICCV 2021 paper SNARF: Differentiable Forward Skinning for Animating Non-rigid Neural Implicit Shapes.

235 Dec 26, 2022
code for generating data set ES-ImageNet with corresponding training code

es-imagenet-master code for generating data set ES-ImageNet with corresponding training code dataset generator some codes of ODG algorithm The variabl

Ordinarabbit 18 Dec 25, 2022
Pytorch-3dunet - 3D U-Net model for volumetric semantic segmentation written in pytorch

pytorch-3dunet PyTorch implementation 3D U-Net and its variants: Standard 3D U-Net based on 3D U-Net: Learning Dense Volumetric Segmentation from Spar

Adrian Wolny 1.3k Dec 28, 2022
Generic ecosystem for feature extraction from aerial and satellite imagery

Note: Robosat is neither maintained not actively developed any longer by Mapbox. See this issue. The main developers (@daniel-j-h, @bkowshik) are no l

Mapbox 1.9k Jan 06, 2023
A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking

PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking PoseRBPF Paper Self-supervision Paper Pose Estimation Video Robot Manipulati

NVIDIA Research Projects 107 Dec 25, 2022
Sample and Computation Redistribution for Efficient Face Detection

Introduction SCRFD is an efficient high accuracy face detection approach which initially described in Arxiv. Performance Precision, flops and infer ti

Sajjad Aemmi 13 Mar 05, 2022
PyTorch implementation of NeurIPS 2021 paper: "CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration"

PyTorch implementation of NeurIPS 2021 paper: "CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration"

76 Jan 03, 2023
Python package for missing-data imputation with deep learning

MIDASpy Overview MIDASpy is a Python package for multiply imputing missing data using deep learning methods. The MIDASpy algorithm offers significant

MIDASverse 77 Dec 03, 2022
Este conversor criará a medida exata para sua receita de capuccino gelado da grandiosa Rafaella Ballerini!

ConversorDeMedidas_CapuccinoGelado Este conversor criará a medida exata para sua receita de capuccino gelado da grandiosa Rafaella Ballerini! Requirem

Arthur Ottoni Ribeiro 48 Nov 15, 2022
Code to reproduce the results in the paper "Tensor Component Analysis for Interpreting the Latent Space of GANs".

Tensor Component Analysis for Interpreting the Latent Space of GANs [ paper | project page ] Code to reproduce the results in the paper "Tensor Compon

James Oldfield 4 Jun 17, 2022
PyTorch implementation of Wide Residual Networks with 1-bit weights by McDonnell (ICLR 2018)

1-bit Wide ResNet PyTorch implementation of training 1-bit Wide ResNets from this paper: Training wide residual networks for deployment using a single

Sergey Zagoruyko 122 Dec 07, 2022
Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.

Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.

Octavio Arriaga 5.3k Dec 30, 2022
Semiconductor Machine learning project

Wafer Fault Detection Problem Statement: Wafer (In electronics), also called a slice or substrate, is a thin slice of semiconductor, such as a crystal

kunal suryawanshi 1 Jan 15, 2022
This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning].

CG3 This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning]. R

12 Oct 28, 2022
Official implementation of YOGO for Point-Cloud Processing

You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module By Chenfeng Xu, Bohan Zhai, Bichen Wu, T

Chenfeng Xu 67 Dec 20, 2022
atmaCup #11 の Public 4th / Pricvate 5th Solution のリポジトリです。

#11 atmaCup 2021-07-09 ~ 2020-07-21 に行われた #11 [初心者歓迎! / 画像編] atmaCup のリポジトリです。結果は Public 4th / Private 5th でした。 フレームワークは PyTorch で、実装は pytorch-image-m

Tawara 12 Apr 07, 2022
MPI Interest Group on Algorithms on 1st semester 2021

MPI Algorithms Interest Group Introduction Lecturer: Steve Yan Location: TBA Time Schedule: TBA Semester: 1 Useful URLs Typora: https://typora.io Goog

Ex10si0n 13 Sep 08, 2022
LoL Runes Recommender With Python

LoL-Runes-Recommender Para ejecutar la aplicación se debe llamar a execute_app.p

Sebastián Salinas 1 Jan 10, 2022
A deep learning network built with TensorFlow and Keras to classify gender and estimate age.

Convolutional Neural Network (CNN). This repository contains a source code of a deep learning network built with TensorFlow and Keras to classify gend

Pawel Dziemiach 1 Dec 19, 2021