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Volume 2019, Article ID 3829263, 12 pages
Research Article

Joint Image Deblurring and Matching with Blurred Invariant-Based Sparse Representation Prior

National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China

Correspondence should be addressed to Nong Sang; nc.ude.tsuh@gnasn

Received 20 August 2019; Accepted 30 September 2019; Published 31 October 2019

Academic Editor: Honglei Xu

Copyright © 2019 Yuanjie Shao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Image matching is important for vision-based navigation. However, most image matching approaches do not consider the degradation of the real world, such as image blur; thus, the performance of image matching often decreases greatly. Recent methods try to deal with this problem by utilizing a two-stage framework—first resorting to image deblurring and then performing image matching, which is effective but depends heavily on the quality of image deblurring. An emerging way to resolve this dilemma is to perform image deblurring and matching jointly, which utilize sparse representation prior to explore the correlation between deblurring and matching. However, these approaches obtain the sparse representation prior in the original pixel space, which do not adequately consider the influence of image blurring and thus may lead to an inaccurate estimation of sparse representation prior. Fortunately, we can extract the pseudo-Zernike moment with blurred invariant from images and obtain a reliable sparse representation prior in the blurred invariant space. Motivated by the observation, we propose a joint image deblurring and matching method with blurred invariant-based sparse representation prior (JDM-BISR), which obtains the sparse representation prior in the robust blurred invariant space rather than the original pixel space and thus can effectively improve the quality of image deblurring and the accuracy of image matching. Moreover, since the dimension of the pseudo-Zernike moment is much lower than the original image feature, our model can also increase the computational efficiency. Extensive experimental results demonstrate that the proposed method performs favorably against the state-of-the-art blurred image matching approach.