Reconstruction of noisy signals by minimization of non-convex functionals

Non-convex functionals have shown sharper results in signal reconstruction as compared to convex ones, although the existence of a minimum has not been established in general. This paper addresses the study of a general class of either convex or non-convex functionals for denoising signals which combines two general terms for fitting and smoothing purposes, respectively. The first one measures how close a signal is to the original noisy signal. The second term aims at removing noise while preserving some expected characteristics in the true signal such as edges and fine details. A theoretical proof of the existence of a minimum for functionals of this class is presented. The main merit of this result is to show the existence of minimizer for a large family of non-convex functionals.The rst author gratefully acknowledges many helpful discussion with Professor H. Frid from IMPA. Also thanks the Promeps Project that support this work. The second author is grateful to the Spanish Ministry of Economy and Competitiveness for the grant TIN2013-46522-P, and to the Generalitat Valenciana for the grant PROMETEOII/2014/062

Variational Approach for the Reconstruction of Damaged Optical Satellite Images Through Their Co-Registration with Synthetic Aperture Radar

In this paper the problem of reconstruction of damaged multi-band opticalimages is studied in the case where we have no information about brightness of suchimages in the damage region. Mostly motivated by the crop field monitoring problem,we propose a new variational approach for exact reconstruction of damaged multi-bandimages using results of their co-registration with Synthetic Aperture Radar (SAR) imagesof the same regions. We discuss the consistency of the proposed problem, give the schemefor its regularization, derive the corresponding optimality system, and describe in detailthe algorithm for the practical implementation of the reconstruction procedure.In this paper the problem of reconstruction of damaged multi-band opticalimages is studied in the case where we have no information about brightness of suchimages in the damage region. Mostly motivated by the crop field monitoring problem,we propose a new variational approach for exact reconstruction of damaged multi-bandimages using results of their co-registration with Synthetic Aperture Radar (SAR) imagesof the same regions. We discuss the consistency of the proposed problem, give the schemefor its regularization, derive the corresponding optimality system, and describe in detailthe algorithm for the practical implementation of the reconstruction procedure

Non-local quasilinear parabolic equations

This is a survey of the most common approaches to quasi-linear parabolic evolution equations, a discussion of their advantages and drawbacks, and a presentation of an entirely new approach based on maximal $L_p$ regularity. The general results here apply, above all, to parabolic initial-boundary value problems that are non-local in time. This is illustrated by indicating their relevance for quasi-linear parabolic equations with memory and, in particular, for time-regularized versions of the Perona–Malik equation of image processing

Trends in Mathematical Imaging and Surface Processing

Motivated both by industrial applications and the challenge of new problems, one observes an increasing interest in the field of image and surface processing over the last years. It has become clear that even though the applications areas differ significantly the methodological overlap is enormous. Even if contributions to the field come from almost any discipline in mathematics, a major role is played by partial differential equations and in particular by geometric and variational modeling and by their numerical counterparts. The aim of the workshop was to gather a group of leading experts coming from mathematics, engineering and computer graphics to cover the main developments

Properties of higher order nonlinear diffusion filtering

This paper provides a mathematical analysis of higher order variational methods and nonlinear diffusion filtering for image denoising. Besides the average grey value, it is shown that higher order diffusion filters preserve higher moments of the initial data. While a maximum-minimum principle in general does not hold for higher order filters, we derive stability in the 2-norm in the continuous and discrete setting. Considering the filters in terms of forward and backward diffusion, one can explain how not only the preservation, but also the enhancement of certain features in the given data is possible. Numerical results show the improved denoising capabilities of higher order filtering compared to the classical methods