4 papers
Mathematical framework for perception-driven parameter choice in image denoising
Saara Isoranta, Emilia L. K. BlÃ¥sten, LÃlian Ferreira de Freitas +3
We approach image denoising from a perception-driven perspective: how can we select the parameters that are best suited for human visual perception? We combine research methods in…
Complex Wavelet-Based Sinogram Segmentation for Metal Artifact Reduction in Cone-Beam CT
Siiri Rautio, Alexander Meaney, Salla-Maaria Latva-Äijö +5
Metal objects pose a significant challenge in cone-beam computed tomography, as their strong and energy-dependent X-ray attenuation leads to inconsistent projections and severe str…
Automatic regularization parameter choice for tomography using a double model approach
Chuyang Wu, Samuli Siltanen
Image reconstruction in X-ray tomography is an ill-posed inverse problem, particularly with limited available data. Regularization is thus essential, but its effectiveness hinges o…
Image Reconstruction in Cone Beam Computed Tomography Using Controlled Gradient Sparsity
Alexander Meaney, Mikael A. K. Brix, Miika T. Nieminen +1
Total variation (TV) regularization is a popular reconstruction method for ill-posed imaging problems, and particularly useful for applications with piecewise constant targets. How…