Analysis of the CCFD method for MC-based image denoising problems

Fairag, Faisal; Chen, Ke; Ahmad, Shahbaz

doi:10.1553/etna_vol54s108

Cited by 11 publications

(1 citation statement)

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“…Because this model converts smooth functions into piecewise constant functions, which create staircase effects in resulting images. To reduce the staircase effects, one remedy is to use the mean curvature (MC) [6][7][8][9][10][11][12][13][14][15][16][17][18] -based regularization models.…”

Section: Introductionmentioning

confidence: 99%

Circulant preconditioners for mean curvature-based image deblurring problem

Ahmad

Fairag

2021

Journal of Algorithms & Computational Technology

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The mean curvature-based image deblurring model is widely used to enhance the quality of the deblurred images. However, the discretization of the associated Euler–Lagrange equations produces a nonlinear ill-conditioned system which affects the convergence of the numerical algorithms such as Krylov subspace methods (generalized minimal residual etc.) To overcome this difficulty, in this paper, we present three new circulant preconditioners. An efficient algorithm is presented for the mean curvature-based image deblurring problem, which combines a fixed point iteration with new preconditioned matrices to handle the nonlinearity and ill-conditioned nature of the large system. The eigenvalues analysis is also presented in the paper. Fast convergence has shown in the numerical results by using the proposed new circulant preconditioners.

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