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Total variation image deblurring with space-varying kernel

Author

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  • Daniel O’Connor

    (University of California Los Angeles)

  • Lieven Vandenberghe

    (University of California Los Angeles)

Abstract

Image deblurring techniques based on convex optimization formulations, such as total-variation deblurring, often use specialized first-order methods for large-scale nondifferentiable optimization. A key property exploited in these methods is spatial invariance of the blurring operator, which makes it possible to use the fast Fourier transform (FFT) when solving linear equations involving the operator. In this paper we extend this approach to two popular models for space-varying blurring operators, the Nagy–O’Leary model and the efficient filter flow model. We show how splitting methods derived from the Douglas–Rachford algorithm can be implemented with a low complexity per iteration, dominated by a small number of FFTs.

Suggested Citation

  • Daniel O’Connor & Lieven Vandenberghe, 2017. "Total variation image deblurring with space-varying kernel," Computational Optimization and Applications, Springer, vol. 67(3), pages 521-541, July.
    • Handle: RePEc:spr:coopap:v:67:y:2017:i:3:d:10.1007_s10589-017-9901-1
    DOI: 10.1007/s10589-017-9901-1
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    References listed on IDEAS

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    1. Laurent Condat, 2013. "A Primal–Dual Splitting Method for Convex Optimization Involving Lipschitzian, Proximable and Linear Composite Terms," Journal of Optimization Theory and Applications, Springer, vol. 158(2), pages 460-479, August.
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