Computerized methods provide analyses of skin lesions from dermoscopy images
automatically. However, the images acquired from dermoscopy devices are noisy and cause …

This first chapter formulates the objectives of compressive sensing. It introduces the
standard compressive problem studied throughout the book and reveals its ubiquity in many …

In medical imaging systems, denoising is one of the important image processing tasks.
Automatic noise removal will improve the quality of diagnosis and requires careful treatment …

Fully updated throughout, with several new chapters, this second edition of Introduction to
Inverse Problems in Imaging guides advanced undergraduate and graduate students in …

At the crossing of the statistical and functional analysis, there exists a relentless quest for an
efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising …

We propose a new first-order splitting algorithm for solving jointly the primal and dual
formulations of large-scale convex minimization problems involving the sum of a smooth …

Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …

Single image super-resolution (SISR) deals with a fundamental problem of upsampling a
low-resolution (LR) image to its high-resolution (HR) version. Last few years have witnessed …

Point clouds obtained with 3D scanners or by image‐based reconstruction techniques are
often corrupted with significant amount of noise and outliers. Traditional methods for point …

We generalize the primal-dual hybrid gradient (PDHG) algorithm proposed by Zhu and
Chan in An Efficient Primal-Dual Hybrid Gradient Algorithm for Total Variation Image …