Learning Discriminative Data Fitting Functions for Blind Image Deblurring

Jinshan Pan, Jiangxin Dong, Yu-Wing Tai, Zhixun Su, Ming-Hsuan Yang; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1068-1076

Abstract


Solving blind image deblurring usually requires defining a data fitting function and image priors. While existing algorithms mainly focus on developing image priors for blur kernel estimation and non-blind deconvolution, only a few methods consider the effect of data fitting functions. In contrast to the state-of-the-art methods that use a single or a fixed data fitting term, we propose a data-driven approach to learn effective data fitting functions from a large set of motion blurred images with associated ground truth blur kernels. The learned data fitting function facilitates estimating accurate blur kernels for generic images and domain-specific problems with corresponding image priors. In addition, we extend the learning approach for data fitting function to latent image restoration and non-uniform deblurring. Extensive experiments on challenging motion blurred images demonstrate the proposed algorithm performs favorably against the state-of-the-art methods.

Related Material


[pdf] [Supp]
[bibtex]
@InProceedings{Pan_2017_ICCV,
author = {Pan, Jinshan and Dong, Jiangxin and Tai, Yu-Wing and Su, Zhixun and Yang, Ming-Hsuan},
title = {Learning Discriminative Data Fitting Functions for Blind Image Deblurring},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}