This book is primarily aimed at students, researchers, and practitioners who are interested in
the computational aspects of X-ray computed tomography (CT). It is also relevant for those …

Sparse subspace clustering (SSC) has emerged as an effective approach for the automatic
analysis of hyperspectral images (HSI). Traditional SSC-based approaches employ the input …

Convex nonsmooth optimization problems, whose solutions live in very high dimensional
spaces, have become ubiquitous. To solve them, the class of first-order algorithms known as …

Quantitative phase imaging (QPI) is an emerging label-free technique that produces images
containing morphological and dynamical information without contrast agents. Unfortunately …

Proximal splitting algorithms are well suited to solving large-scale nonsmooth optimization
problems, in particular those arising in machine learning. We propose a new primal-dual …

In this work, we study a general framework of discrete approximations of the total variation
for image reconstruction problems. The framework, for which we can show consistency in …

We consider minimizing the sum of three convex functions, where the first one F is smooth,
the second one is nonsmooth and proximable and the third one is the composition of a …

Traditional partition-based clustering is very sensitive to the initialized centroids, which are
easily stuck in the local minimum due to their nonconvex objectives. To this end, convex …

Image restoration remains a challenging task in image processing. Numerous methods
tackle this problem, which is often solved by minimizing a nonsmooth penalized co-log …

Establishing certified uncertainty quantification (UQ) in imaging processing applications
continues to pose a significant challenge. In particular, such a goal is crucial for accurate …