We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in the field. We adopt a variational perspective and specify the proposed sparsity-promoting data reconstruction models as energy minimization pr...

Sparse-view scanning has great potential for realizing ultra-low-dose computed tomography (CT) examination. However, noise and artifacts in reconstructed images are big obstacles, which must be handled to maintain the diagnosis accuracy. Existing sparse-view CT reconstruction algorithms were usually designed circular imaging geometry, whereas helical geometry is commonly adopted clinic. In this...

This supplementary document provides further visualizations, implementation details and analysis of convergence of our method. 1 Method Implementation Details Visualizing spatially varying sparsity Our method produces a set of sparse deformation components by analyzing an input mesh animation. Compared to methods like [Tena et al. 2011] and [Kavan et al. 2010], which in principle can also be vi...

The pioneering work on parameter orthogonalization by Cox and Reid is presented as an inducement of abstract population-level sparsity. This taken a unifying theme for this article, in which sparsity-inducing parameterizations or data transformations are sought. Three recent examples framed light: sparse covariance models, the construction factorizable elimination nuisance parameters, inference...

Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used widely in the SU problem. One of the constraints which w...

The Singular Value Decomposition (SVD) is a longstanding standard for data approximation because it is optimal in the 2 and Frobenius norms. The SVD, nevertheless, suffers from many setbacks, including computational cost, loss of sparsity in the decomposition, and the inability to be updated easily when new information arrives. Additionally, the SVD provides limited information on data features...