@inproceedings{6189abb2554c485f900eb66236cd3c6a,
title = "A second-order approach for blind motion deblurring by normalized l1 regularization",
abstract = "We propose a second-order approach for blind motion deblurring. Our idea is to define an energy functional, and the convolution kernel corresponds to the minimum of the functional. After the kernel is obtained, the problem is solved by existing non-blind deconvolution algorithms. To avoid that the minimizer of energy functional does not correspond to the unblurred image, which is often encountered in many algorithms, in the literature the normalized l1 norm regularization term for the original or first-order gradient image was adopted. We further extend the idea using the second-order gradient image, which is the main novelty of the paper. This method favours a piecewise linear transition in the unblurred image, and thus efficiently attenuates the staircase and ring effects in the original or first-order case. Comparing with other stateof- the-art algorithms, the proposed method is effective in estimating the blur-kernel and restoring the unblurred image.",
keywords = "Blind motion deblurring, Normalized l norm, Secondorder gradient, Staircase effects",
author = "Zedong Chen and Faming Fang and Yingying Xu and Chaomin Shen",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 17th Pacific-Rim Conference on Multimedia, PCM 2016 ; Conference date: 15-09-2016 Through 16-09-2016",
year = "2016",
doi = "10.1007/978-3-319-48896-7\_29",
language = "英语",
isbn = "9783319488950",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "296--305",
editor = "Enqing Chen and Yun Tie and Yihong Gong",
booktitle = "Advances in Multimedia Information Processing – 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings",
address = "德国",
}