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 the associated ground truth blur kernels. The learned data fitting function facilitates estimating accurate blur kernels for generic scenes and domain-specific problems with corresponding image priors. In addition, we extend the learning approach for data fitting function to latent image restoration and nonuniform deblurring. Extensive experiments on challenging motion blurred images demonstrate the proposed algorithm performs favorably against the state-of-the-art methods.
|Title of host publication||Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||9|
|Publication status||Published - 2017 Dec 22|
|Event||16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy|
Duration: 2017 Oct 22 → 2017 Oct 29
|Name||Proceedings of the IEEE International Conference on Computer Vision|
|Other||16th IEEE International Conference on Computer Vision, ICCV 2017|
|Period||17/10/22 → 17/10/29|
Bibliographical noteFunding Information:
This work has been partially supported by NSFC (No. 61572099, 61522203), NSF CAREER (No. 1149783), 973 Program (No. 2014CB347600), NSF of Jiangsu Province (No. BK20140058), the National Key R&D Program of China (No. 2016YFB1001001), and gifts from Adobe and Nvidia.
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition