FastDVDnet

A state-of-the-art, simple and fast network for Deep Video Denoising which uses no motion compensation

Previous deep video denoising algorithm: DVDnet

Overview

This source code provides a PyTorch implementation of FastDVDnet image denoising, 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).

Video Examples

You can download several denoised sequences with our algorithm and other methods here

Running Times

FastDVDnet is orders of magnitude faster than other state-of-the-art methods

User Guide

The code as is runs in Python +3.6 with the following dependencies:

Dependencies

Usage

Testing

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
run with --help to see details on all input parameters

Training

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