X-rays image reconstruction using proximal algorithm and adapted TV regularization

Abstract

Computed tomography (CT) aims to reconstruct an internal distribution of an object based on projection measurements . In the case of a limited number of projections, the reconstruction problem becomes significantly ill-posed. Practically, reconstruction algorithms play a crucial role in overcoming this problem. In the case of missing or incomplete data, and in order to improve the quality of the reconstruction image, the choice of a sparse regularisation by adding l1 norm is needed. The reconstruction problem is then based on using proximal operators. We are interested in the Douglas-Rachford method and employ total variation (TV) regularization. An efficient technique based on these concepts is proposed in this study. The primary goal is to achieve highquality reconstructed images in terms of PSNR parameter and relative error. The numerical simulation results demonstrate that the suggested technique minimizes noise and artifacts while preserving structural information. The results are encouraging and indicate the effectiveness of the proposed strategy.

A non-local total generalized variation regularization reconstruction method for sparse-view x-ray CT

“…Although the TV regularization method has achieved good reconstruction performance, certain limitations still exist [13,14]. In the past decade, various variants of TV regularization terms have been proposed, such as multi-directional TV [15], gradient directional TV [16], weighted TV [17,18], relative TV [19,20], and others [21][22][23][24][25][26][27][28]. One of the drawbacks of TV regularization is the potential occurrence of undesired patchy artifacts in reconstructed images, which can hinder the preservation of smooth transition structures.…”