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arXiv · 2309.05204

Accelerated Proximal Iterative re-Weighted $\ell_1$ Alternating Minimization for Image Deblurring

Tarmizi Adam·Alexander Malyshev·Mohd Fikree Hassan·Nur Syarafina Mohamed·Md Sah Hj Salam
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Abstract

The quadratic penalty alternating minimization (AM) method is widely used for solving the convex $\ell_1$ total variation (TV) image deblurring problem. However, quadratic penalty AM for solving the nonconvex nonsmooth $\ell_p$, $0 < p < 1$ TV image deblurring problems is less studied. In this paper, we propose two algorithms, namely proximal iterative re-weighted $\ell_1$ AM (PIRL1-AM) and its accelerated version, accelerated proximal iterative re-weighted $\ell_1$ AM (APIRL1-AM) for solving the nonconvex nonsmooth $\ell_p$ TV image deblurring problem. The proposed algorithms are derived from the proximal iterative re-weighted $\ell_1$ (IRL1) algorithm and the proximal gradient algorithm. Numerical results show that PIRL1-AM is effective in retaining sharp edges in image deblurring while APIRL1-AM can further provide convergence speed up in terms of the number of algorithm iterations and computational time.

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Tarmizi Adam, Alexander Malyshev, Mohd Fikree Hassan, Nur Syarafina Mohamed, Md Sah Hj Salam. 2023-09-11. Accelerated Proximal Iterative re-Weighted $\ell_1$ Alternating Minimization for Image Deblurring. https://arxiv.org/abs/2309.05204

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