Testable uniqueness conditions for empirical assessment of undersampling levels in total variation-regularized X-ray CT

摘要: We study recoverability in fan-beam computed tomography (CT) with sparsity and total variation priors: how many underdetermined linear measurements suffice for recovering images of given sparsity? Results from compressed sensing (CS) establish such conditions example random measurements, but not CT. Recoverability is typically tested by checking whether a solution recovers the original. This approach cannot guarantee uniqueness decision therefore depends on optimization algorithm. propose new computational methods to test verifying conditions. Using both reconstruction testing, we empirically number CT sufficient recovery classes sparse images. demonstrate an average-case relation between sampling observe sharp phase transition as known CS, never established In addition assessing mor...