English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Spectral computed tomography (CT) can reconstruct spectral images fromdifferent energy bins using photon counting detectors (PCDs). However, due tothe limited photons and counting rate in the corresponding spectral fraction,the reconstructed spectral images usually suffer from severe noise. In thispaper, a fourth-order nonlocal tensor decomposition model for spectral CT imagereconstruction (FONT-SIR) method is proposed. Similar patches are collected inboth spatial and spectral dimensions simultaneously to form the basic tensorunit. Additionally, principal component analysis (PCA) is applied to extractlatent features from the patches for a robust and efficient similarity measure.Then, low-rank and sparsity decomposition is performed on the producedfourth-order tensor unit, and the weighted nuclear norm and total variation(TV) norm are used to enforce the low-rank and sparsity constraints,respectively. The alternating direction method of multipliers (ADMM) is adoptedto optimize the objective function. The experimental results with our proposedFONT-SIR demonstrates a superior qualitative and quantitative performance forboth simulated and real data sets relative to several state-of-the-art methods,in terms of noise suppression and detail preservation.

Fourth-Order Nonlocal Tensor Decomposition Model for Spectral Computed Tomography