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FastDVDnet: A Very Fast Deep Video Denoising algorithm
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README.md

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

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

ABOUT

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 matias dot tassano at parisdescartes dot fr
  • Copyright : (C) 2019 Matias Tassano
  • Licence : GPL v3+, see GPLv3.txt

The sequences are Copyright GoPro 2018

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