Deep Multi-scale CNN for Dynamic Scene Deblurring
Switch branches/tags
Nothing to show
Clone or download
Permalink
Failed to load latest commit information.
code enable half precision, fix demo Oct 25, 2018
dataset enable half precision, fix demo Oct 25, 2018
experiment examples added Apr 20, 2017
images Initial commit Dec 8, 2016
.gitignore examples added Apr 20, 2017
README.md typo fix Oct 25, 2018

README.md

DeepDeblur_release

Single image deblurring with deep learning.

This is a project page for our research. Please refer to our CVPR 2017 paper for details:

Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring [paper] [supplementary] [slide]

If you find our work useful in your research or publication, please cite our work:

@InProceedings{Nah_2017_CVPR,
  author = {Nah, Seungjun and Kim, Tae Hyun and Lee, Kyoung Mu},
  title = {Deep Multi-Scale Convolutional Neural Network for Dynamic Scene Deblurring},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {July},
  year = {2017}
}

Dependencies

  luarocks install torchx
  • cudnn

Code

To run demo, download and extract the trained models into "experiment" folder.

Type following command in "code" folder.

qlua -i demo.lua -load -save release_scale3_adv_gamma -blur_type gamma2.2 -type cudaHalf
qlua -i demo.lua -load -save release_scale3_adv_lin -blur_type linear -type cudaHalf

To train a model, clone this repository and download below dataset in "dataset" directory.

The data structure should look like "dataset/GOPRO_Large/train/GOPRxxxx_xx_xx/blur/xxxxxx.png"

Then run main.lua in "code" directory with optional parameters.

th main.lua -nEpochs 450 -save scale3 # Train for 450 epochs, save in 'experiment/scale3'
th main.lua -load -save scale3  # Load saved model
> blur_dir, output_dir = ...
> deblur_dir(blur_dir, output_dir)

Optional parameters are listed in opts.lua

ex) -type: Operation type option. Supports cuda and cudaHalf. Half precision CNN has similar accuracy as single precision in evaluation mode. However, fp16 training is not meant to be supported in this code. ADAM optimizer is hard to use with fp16.

Dataset

In this work, we proposed a new dataset of realistic blurry and sharp image pairs using a high-speed camera. However, we do not provide blur kernels as they are unknown.

Statistics Training Test Total
sequences 22 11 33
image pairs 2103 1111 3214

Download links

  • GOPRO_Large : Blurry and sharp image pairs. Blurry images includes both gamma corrected and not corrected (linear CRF) versions.
  • GOPRO_Large_all : All the sharp images used to generate blurry images. You can generate new blurry images by accumulating differing number of sharp frames.

Here are some examples.

Blurry image example 1 Blurry image

Sharp image example 1 Sharp image

Blurry image example 2 Blurry image

Sharp image example 2 Sharp image

Acknowledgement

This project is partially funded by Microsoft Research Asia