This code is the upgraded implementation of our TIP paper "Graph-based Blind Image Deblurring from a Single Photograph".

Matlab(>=R2015a)

Step 1. run graph_blind_main.m
Step 2. select a blurred image

Users only need to tune ONE parameter. On line 21, the estimated kernel size k_estimate_size.

• The k_estimate_size must be LARGER than the real kernel size (The default value is 69).
• In order to have the best performance, please set the value close to real kernel size and slightly larger.

If you want to turn off the intermediate output, you can set show_intermediate=false on line 22.

In order to be more robust with noise, we add several denoising modules beyond the paper.

• We embed a TV denoising to pre-process the input image.
• We add a wavelet domain filtering for intermediate output kernels.
• We add a mask to filter small/noisy gradient in the gradient domain.

More sophisticated denoising, such as BM3D, can be done by users in advance.

After kernel estimation with the proposed algorithm, we use the state-of-the-art methods to do non-blind image deblurring. Here, we provide users with [1] to do the following non-blind image deblurring process. Users can also employ [2] or the non-blind deblurring method in [3], by themselves.

[1] D. Krishnan and R. Fergus, “Fast image deconvolution using hyperlaplacian priors,” in Proceedings of Neural Information Processing Systems, 2009, Conference Proceedings, pp. 1033–1041.

[2] D. Zoran and Y. Weiss, “From learning models of natural image patches to whole image restoration,” in Proceedings of IEEE International Conference on Computer Vision, 2011, Conference Proceedings, pp. 479–486.

[3] J. Pan, D. Sun, H. Pfister, and M.-H. Yang, “Blind image deblurring using dark channel prior,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, June 2016.

@ARTICLE{GraphBID, 
author={Y. Bai and G. Cheung and X. Liu and W. Gao}, 
journal={IEEE Transactions on Image Processing}, 
title={Graph-Based Blind Image Deblurring From a Single Photograph}, 
year={2019}, 
volume={28}, 
number={3}, 
pages={1404-1418}, 
doi={10.1109/TIP.2018.2874290}, 
ISSN={1057-7149}, 
month={March},}