Please use this identifier to cite or link to this item: 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
Keywords: Camera shake
Image deblurring
Non-uniform deblurring
Blind deconvolution
Fast Fourier transform
Issue Date: 2016
Source: EURASIP journal on advances in signal processing, 2016, p.1-22 How to cite?
Journal: EURASIP journal on advances in signal processing 
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.
URI: http://hdl.handle.net/10397/68560
ISSN: 1687-6172
EISSN: 1687-6180
DOI: 10.1186/s13634-016-0318-2
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