fedral / bsvd

[ACM MM 2022] Real-time Streaming Video Denoising with Bidirectional Buffers

BSVD

Official implementation for Real-time Streaming Video Denoising with Bidirectional Buffers (ACM MM 2022)

project website | paper

This branch is for synthesic noise removal on DAVIS dataset. We also provide the code for real raw noise removal here.

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Dependencies and Installation

Environment

This code is based on PyTorch and BasicSR repo. It has been tested on Ubuntu 18.04 LTS and CUDA 11.1.

Create a new conda environment with:

conda create --name bsvd python=3.7
conda activate bsvd

We recommend you install this repo locall as:

pip install -r requirements.txt
cd BasicSR
python setup.py develop
cd ..

if you use CUDA 11 and get runtime error. Reinstall torch as:

pip uninstall torch
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

We use Nvidia DALI loader to load video frames.

pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.12.0

Our test environment is CUDA 11.1. You may find DALI loader for other CUDA version from their github

Data

Download the DAVIS (for train and test) and Set8 dataset (for test) from onedrive. Keep the folder structure as

--bsvd
 |--BasicSR
 |--datasets
    |--DAVIS-training-mp4
    |--DAVIS-2017-test-dev-480p
    |--Set8
 |--Experimental_root
 |--***.py

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Test

You may also download the pretrained checkpoint Put bsvd_64.pth under .experiments/pretrained_ckpt. Then, run the command to produce the result in the Table2 from paper

python ./run_test.py -opt ./options/test/bsvd_c64.yml

Two checkpoints are also provided for blind and unidirection

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Train

CUDA_VISIBLE_DEVICES=0,1 python ./run.py -opt ./options/train/bsvd_c64_unblind.yml

We train the model on 2 RTX 3090 GPU for 2 days. Here is one example log and tensorboard for our training.

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Profile the model

Type in command line

python profile.py

Example output

__file__ profile.py
Disable distributed.
device_name CUDA
os.environ["CUDA_VISIBLE_DEVICES"] 2
device cuda
load from ./experiments/pretrained_ckpt/bsvd-64.pth
<Experimental_root.models.denoising_model.DenoisingModel object at 0x7f3187110710>
size of tensor torch.Size([1, 10, 4, 540, 960])
use device  cuda
output shape is torch.Size([1, 10, 3, 540, 960])
size of tensortorch.Size([1, 10, 4, 540, 960])
use device cuda

test function name: <class 'Experimental_root.archs.bsvd_arch.BSVD'>
10 loops, mean of best 1: 0.353594 sec per loop
max memory required              2.26GB

Codes Development

We support all functions provided by BasicSR natively. You may check the docs of BasicSR for details of file structure and installation.

To extend functions, please create your codes here:

/Experimental_root
|- models       # Custom Training and validation Behaviour
|- archs        # Custom network architectures
|- data         # Custom dataloader

Citation

If you find this work useful for your research, please cite:

@inproceedings{qi2022BSVD,
    title={Real-time Streaming Video Denoising with Bidirectional Buffers},
    author={Chenyang Qi and Junming Chen and Xin Yang and Qifeng Chen},
    booktitle = {ACM MM},
    year={2022}
    }

Contact

Please contact us if there is any question (Chenyang QI, [email protected]; Junming CHEN, [email protected])