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FastDVDNet

The model file has released at here!

TODO LIST:

  1. write the code of single-stage model, single-scale model to verify the superiority of FastDVDNet

Introduction

This repo. is an unofficial version of FastDVDNet:ToWards Real-Time Video Denoising Without Explicit Motion Estimation by making use of PyTorch.

Dataset

The dataset of Vimeo-90K is employed for training, whose size is about 82G. The dataset consists of about 90K videos and all of them include 7 frames with large motion.

You can choose the dataset you like to train or validate the effectiveness of FastDVDNet, such as the dataset indicated in the paper (I don't want to spend more time downloading it so I choose the Vimeo-90K.).

Requirements

  1. PyTorch>=1.0.0
  2. Numpy
  3. scikti-image
  4. tensorboardX (for visualization of loss, PSNR and SSIM)

Usage

  1. Run pip install -r requirements.txt firstly to install the packages.
  2. data_provider.py is the code for loading data from dataset. You can modify the code to adapt your dataset if it is not Vimeo-90K.
  3. train_eval.py is the code for train and validation process. The validation process is activated by --eval, otherwise, it is work on the train mode.
  4. Script for restarting the model can be follow,
    CUDA_VISIBLE_DEVICES=1,2 python train_eval.py --cuda -nw 16 --frames 5 -s 96 -bs 64 -lr 1e-4 --restart
  5. As for validation mode,
    CUDA_VISIBLE_DEVICES=1,2 python train_eval.py --cuda --eval for this mode, the command -s is unavailable, image size is default 448*256.

Details

train_eval.py [-h] [--dataset_path DATASET_PATH] [--txt_path TXT_PATH]
                     [--batch_size BATCH_SIZE] [--frames FRAMES]
                     [--im_size IM_SIZE] [--learning_rate LEARNING_RATE]
                     [--num_worker NUM_WORKER] [--restart] [--eval] [--cuda]
                     [--max_epoch MAX_EPOCH]

optional arguments:
  -h, --help            show this help message and exit
  --dataset_path DATASET_PATH, -dp DATASET_PATH
                        the path of vimeo-90k
  --txt_path TXT_PATH, -tp TXT_PATH
                        the path of train/eval txt file
  --batch_size BATCH_SIZE, -bs BATCH_SIZE
                        batch size
  --frames FRAMES, -f FRAMES
  --im_size IM_SIZE, -s IM_SIZE
  --learning_rate LEARNING_RATE, -lr LEARNING_RATE
  --num_worker NUM_WORKER, -nw NUM_WORKER
                        number of workers to load data by dataloader
  --restart, -r         whether to restart the train process
  --eval, -e            whether to work on the eval mode
  --cuda                whether to train the network on the GPU, default is
                        mGPU
  --max_epoch MAX_EPOCH

Results

The model is trained on a TITAN Xp GPU with patch size of 96*96 and total 70000 iterations.

Several groups of simulated images are uploaded at here. The FastDVDNet could work well based on the Vimeo-90K dataset totally, one of results is shown as

ground of truth

ground-of-truth

noisy reference

noisy-ref

predicted image

pred-image

However, there are still some artifacts existing, for example, the left side of this image has a large area of green spots. I will investigate the reason of this.

References

  1. FastDVDNet:ToWards Real-Time Video Denoising Without Explicit Motion Estimation
  2. DVDNet: A Fast Network For Deep Video Denoising
  3. Code of U-Net is based on my previous work

Support me by starring or forking this repo., please.