Aimed model

FITVNet(https://arxiv.org/pdf/2001.00346.pdf)

Baseline codes

Baseline codes comes from (https://github.com/m-tassano/fastdvdnet)

official implementation of FastDVDnet(https://arxiv.org/pdf/1907.01361.pdf)

User Guide

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

Dependencies

• PyTorch v1.0.0
• NVIDIA DALI
• scikit-image
• numpy
• OpenCV
• tensorboardX

Note: the tested version of NVIDIA DALI is 0.10.0. To install it (supposing you have CUDA 10.0), you need to run

pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/cuda/10.0 nvidia-dali==0.10.0 

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
• to denoise clipped AWGN run with --model_file model_clipped_noise.pth
• 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