A state-of-the-art, simple and fast network for Deep Video Denoising which uses no motion compensation.
NEW: Paper to be presented at CVPR2020
Previous deep video denoising algorithm: DVDnet
This source code provides a PyTorch implementation of the FastDVDnet video denoising algorithm, as in Tassano, Matias and Delon, Julie and Veit, Thomas. "FastDVDnet: Towards Real-Time Video Denoising Without Explicit Motion Estimation", arXiv preprint arXiv:1907.01361 (2019).
You can download several denoised sequences with our algorithm and other methods here
FastDVDnet is orders of magnitude faster than other state-of-the-art methods
The code as is runs in Python +3.6 with the following dependencies:
If you want to denoise an image sequence using the pretrained model you can execute
test_fastdvdnet.py \
--test_path <path_to_input_sequence> \
--noise_sigma 30 \
--save_path results
NOTES
- The image sequence should be stored under <path_to_input_sequence>
- The model has been trained for values of noise in [5, 55]
- run with --no_gpu to run on CPU instead of GPU
- run with --save_noisy to save noisy frames
- set max_num_fr_per_seq to set the max number of frames to load per sequence
- to denoise clipped AWGN run with *--model_file model_clipped_noise.pth"
- run with --help to see details on all input parameters
If you want to train your own models you can execute
train_fastdvdnet.py \
--trainset_dir <path_to_input_mp4s> \
--valset_dir <path_to_val_sequences> \
--log_dir logs
NOTES
- As the dataloader in based on the DALI library, the training sequences must be provided as mp4 files, all under <path_to_input_mp4s>
- The validation sequences must be stored as image sequences in individual folders under <path_to_val_sequences>
- run with --help to see details on all input parameters
Copying and distribution of this file, with or without modification, are permitted in any medium without royalty provided the copyright notice and this notice are preserved. This file is offered as-is, without any warranty.
- Author : Matias Tassano
mtassano at gopro dot com - Copyright : (C) 2019 Matias Tassano
- Licence : GPL v3+, see GPLv3.txt
The sequences are Copyright GoPro 2018
