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http://hdl.handle.net/10397/68560
Title: | Efficient non-uniform deblurring based on generalized additive convolution model | Authors: | Deng, H Ren, D Zhang, D Zuo, W Zhang, H Wang, K |
Issue Date: | 2016 | Source: | EURASIP journal on advances in signal processing, 2016, v. 2016, 22, p. 1-22 | Abstract: | Image with non-uniform blurring caused by camera shake can be modeled as a linear combination of the homographically transformed versions of the latent sharp image during exposure. Although such a geometrically motivated model can well approximate camera motion poses, deblurring methods in this line usually suffer from the problems of heavy computational demanding or extensive memory cost. In this paper, we develop generalized additive convolution (GAC) model to address these issues. In GAC model, a camera motion trajectory can be decomposed into a set of camera poses, i.e., in-plane translations (slice) or roll rotations (fiber), which can both be formulated as convolution operation. Moreover, we suggest a greedy algorithm to decompose a camera motion trajectory into a more compact set of slices and fibers, and together with the efficient convolution computation via fast Fourier transform (FFT), the proposed GAC models concurrently overcome the difficulties of computational cost and memory burden, leading to efficient GAC-based deblurring methods. Besides, by incorporating group sparsity of the pose weight matrix into slice GAC, the non-uniform deblurring method naturally approaches toward the uniform blind deconvolution. | Keywords: | Camera shake Image deblurring Non-uniform deblurring Blind deconvolution Fast Fourier transform |
Publisher: | Springer | Journal: | EURASIP journal on advances in signal processing | ISSN: | 1687-6172 | EISSN: | 1687-6180 | DOI: | 10.1186/s13634-016-0318-2 | Rights: | © 2016 Deng et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in anymedium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commonslicense, and indicate if changes were made. The following publication Deng, H., Ren, D., Zhang, D., Zuo, W., Zhang, H., & Wang, K. (2016). Efficient non-uniform deblurring based on generalized additive convolution model. Eurasip Journal on Advances in Signal Processing, 2016, 22, 1-22 is available at https://dx.doi.org/10.1186/s13634-016-0318-2 |
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