Single image deblurring with deep learning.

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

Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring

• torch7
• torchx
• cudnn

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
qlua -i demo.lua -load -save release_scale3_adv_lin -blur_type linear

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.

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

optional parameters are listed in opts.lua

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.

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