In X-ray tomography image reconstruction, one of the most successful approaches involves a statistical approach with l 2 norm for fidelity function and some regularization function with l p norm, 1 < p < 2 . Among them stands out, both for its results and the computational performance, a technique that involves the alternating minimization of an objective function with l 2 norm for fidelity and a regularization term that uses discrete gradient transform (DGT) sparse transformation minimized by total variation (TV). This work proposes an improvement to the reconstruction process by adding a bilateral edge-preserving (BEP) regularization term to the objective function. BEP is a noise reduction method and has the purpose of adaptively eliminating noise in the initial phase of reconstruction. The addition of BEP improves optimization of the fidelity term and, as a consequence, improves the result of DGT minimization by total variation. For reconstructions with a limited number of projections (low-dose reconstruction), the proposed method can achieve higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) results because it can better control the noise in the initial processing phase.
Edge-Preserving Image Smoothing Via a Total Variation Regularizer and a Nonconvex Regularizer
