arXiv subjects

Abdulmajeed Alsubhi

Publications and source records attributed to Abdulmajeed Alsubhi.

3 recordsLinked to original sources

arXiv2026

Hybrid GKB Methods for X-Ray Tomography Problems with Unmatched Back Projector

X ray Computed Tomography (CT) is a widely used imaging modality in medical, industrial, and scientific applications. In practical CT implementations, forward, A, and back projection, B, operators are often constructed using different discretization schemes to improve computational efficiency on available software and hardware platforms. Consequently, the resulting projector pair is generally unmatched, meaning that the back projector is not the exact adjoint of the forward projector. This mismatch alters the mathematical properties of the reconstruction problem and can affect the convergence behavior of iterative reconstruction methods. Previous studies have proposed AB and BA Golub Kahan bidiagonalization (GKB) methods, as well as GMRES and hybrid GMRES methods, for solving CT reconstruction problems with unmatched projector pairs, demonstrating reduced semiconvergence effects compared with conventional approaches. In this work, we develop hybrid variants of the AB- and BA-GKB algorithms by incorporating regularization within the projection process. The singular value decomposition for the operator on the projected space is used to efficiently reconstruct the solution, and to automatically select the regularization parameter using either the L-curve or generalized cross-validation. Numerical experiments on several CT reconstruction problems demonstrate the effectiveness of the proposed hybrid methods in improving robustness against semiconvergence.

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arXiv2026

GKB Methods for X-Ray Computed Tomography with an Unmatched Back Projector

In large scale X ray Computed Tomography (CT) inverse problems, the forward and back projectors are often generated using different discretizations. This discrepancy leads to unmatched pairs of projections, resulting in inconsistent normal equations. Consequently, employing the Conjugate Gradient method does not produce a useful solution. For matched operator pairs, the Golub Kahan bidiagonalization (GKB) method provides an efficient solution strategy. It works by projecting the original large-scale problem onto a lower-dimensional subspace, enabling the solution to be computed via a singular value decomposition of a sparse lower bidiagonal matrix. To address unmatched-pair problems in CT, we propose the AB and BA GKB algorithms as preconditioned forms of the GKB. These methods are straightforward to implement and allow for parameter tuning. We provide a discussion on the theoretical computational costs of our proposed algorithms in terms of floating point operations and compare with existing methods. While many Krylov methods tend to amplify noise in solutions, leading to semiconvergence, our proposed algorithms demonstrate greater resilience against this effect. We validate the effectiveness of our approach through numerical examples across various CT problems, showcasing its ability to deliver more stable solutions.

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arXiv2024

Split Bregman Isotropic and Anisotropic Image Deblurring with Kronecker Product Sum Approximations using Single Precision Enlarged-GKB or RSVD Algorithms to provide low rank truncated SVDs

We consider the solution of the $\ell_1$ regularized image deblurring problem using isotropic and anisotropic regularization implemented with the split Bregman algorithm. For large scale problems, we replace the system matrix $A$ using a Kronecker product approximation obtained via an approximate truncated singular value decomposition for the reordered matrix $\mathcal{R}(A)$. To obtain the approximate decomposition for $\mathcal{R}(A)$ we propose the enlarged Golub Kahan Bidiagonalization algorithm that proceeds by enlarging the Krylov subspace beyond either a given rank for the desired approximation, or uses an automatic stopping test that provides a suitable rank for the approximation. The resultant expansion is contrasted with the use of the truncated and the randomized singular value decompositions with the same number of terms. To further extend the scale of problem that can be considered we implement the determination of the approximation using single precision, while performing all steps for the regularization in standard double precision. The reported numerical tests demonstrate the effectiveness of applying the approximate single precision Kronecker product expansion for $A$, combined with either isotropic or anisotropic regularization implemented using the split Bregman algorithm, for the solution of image deblurring problems. As the size of the problem increases, our results demonstrate that the major costs are associated with determining the Kronecker product approximation, rather than with the cost of the regularization algorithm. Moreover, the enlarged Golub Kahan Bidiagonalization algorithm competes favorably with the randomized singular value decomposition for estimating the approximate singular value decomposition.

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