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

Optimization methods are at the core of many problems in signal/image processing,
computer vision, and machine learning. For a long time, it has been recognized that looking …

A large number of imaging problems reduce to the optimization of a cost function, with
typical structural properties. The aim of this paper is to describe the state of the art in …

Hyperspectral remote sensing images (HSIs) usually have high spectral resolution and low
spatial resolution. Conversely, multispectral images (MSIs) usually have low spectral and …

In this work, we study the computational complexity of reducing the squared gradient
magnitude for smooth minimax optimization problems. First, we present algorithms with …

We revisit the proofs of convergence for a first order primal–dual algorithm for convex
optimization which we have studied a few years ago. In particular, we prove rates of …

Operator-splitting methods convert optimization and inclusion problems into fixed-point
equations; when applied to convex optimization and monotone inclusion problems, the …

In this paper, we propose an inertial forward-backward splitting algorithm to compute a zero
of the sum of two monotone operators, with one of the two operators being co-coercive. The …

On solving a convex-concave bilinear saddle-point problem (SPP), there have been many
works studying the complexity results of first-order methods. These results are all about …

We introduce a new paradigm for solving regularized variational problems. These are
typically formulated to address ill-posed inverse problems encountered in signal and image …