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

• torch7
• torchx

  luarocks install torchx

• cudnn

Code

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

• models

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

Sharp image example 1

Blurry image example 2

Sharp image example 2

Acknowledgement

This project is partially funded by Microsoft Research Asia