CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration

In this paper, we propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms, with a special focus for image processing tasks. Generalizing ideas that emerged for $\ell_1$ regularization, we develop an approach re-fitting the results of standard methods towards the input data. Total variation regularizations and non-local means are special cases of interest. We identify important covariant information that should be preserved by the re-fitting method, and emphasize the importance of preserving the Jacobian (w.r.t. the observed signal) of the original estimator. Then, we provide an approach that has a "twicing" flavor and allows re-fitting the restored signal by adding back a local affine transformation of the residual term. We illustrate the benefits of our method on numerical simulations for image restoration tasks

Growers’ risk perception and trust in control options for huanglongbing citrus-disease in Florida and California

Citrus huanglongbing disease is an acute bacterial disease that threatens the sustainability of citrus production across the world. In the USA, the Asian Citrus Psyllid (ACP) is responsible for spreading the disease. Successful suppression of HLB requires action against ACP at large spatial scales, i.e. growers must cooperate. In Florida and California, the regions in which citrus is grown have been split into management areas and growers are encouraged to coordinate spraying of insecticide across these (area-wide control). We surveyed growers from Florida and California to assess the consensus of opinions concerning issues that influence HLB management. Our results show that risk perception and trust in control options are central to the decision by growers on whether to join an area-wide control program. Growers’ perceptions on risk and control efficacy are influenced by information networks and observations about the state of the epidemic and psyllid populations. Researchers and extension agents were reported to have the largest influence on these perceptions. Differences in opinion between California and Florida growers as to the efficacy of treatments were largely a function of experience. A large proportion of growers identified failure of participation as a reason why participation in area-wide control might not occur

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem

Communicating content: development and evaluation of icons for academic document triage through visualisation and perception

This work seeks to identify key features and characteristics for the design of icons that can support the tasks of information seekers in academic document triage interfaces. Such icons are meant to act as visual links to the specific elements or sections in an academic document. We suggest that icons in triage interfaces are better able to communicate information, provide feedback and enable faster user interactions than text, particularly in mobile-based interfaces. Through investigation of visualisation and perception processes, we are able to propose five primary icon categories, the two most dominant being iconic and symbolic: iconic representations mostly apply to graphically and spatially distinct document elements (i.e. Title, Abstract, Tables and Figures), externalising the elements’ surface propositions. Symbolic representations are largely associated with elements of greater semantic value (Introduction, Conclusion, Full text and Author), drawing upon the elements’ deep propositions

Reduced voltage losses yield 10% efficient fullerene free organic solar cells with >1 V open circuit voltages

Optimization of the energy levels at the donor–acceptor interface of organic solar cells has driven their efficiencies to above 10%. However, further improvements towards efficiencies comparable with inorganic solar cells remain challenging because of high recombination losses, which empirically limit the open-circuit voltage (Voc) to typically less than 1 V. Here we show that this empirical limit can be overcome using non-fullerene acceptors blended with the low band gap polymer PffBT4T-2DT leading to efficiencies approaching 10% (9.95%). We achieve Voc up to 1.12 V, which corresponds to a loss of only Eg/q − Voc = 0.5 ± 0.01 V between the optical bandgap Eg of the polymer and Voc. This high Voc is shown to be associated with the achievement of remarkably low non-geminate and non-radiative recombination losses in these devices. Suppression of non-radiative recombination implies high external electroluminescence quantum efficiencies which are orders of magnitude higher than those of equivalent devices employing fullerene acceptors. Using the balance between reduced recombination losses and good photocurrent generation efficiencies achieved experimentally as a baseline for simulations of the efficiency potential of organic solar cells, we estimate that efficiencies of up to 20% are achievable if band gaps and fill factors are further optimized

Tail state limited photocurrent collection of thick photoactive layers in organic solar cells

We analyse organic solar cells with four different photoactive blends exhibiting differing dependencies of short-circuit current upon photoactive layer thickness. These blends and devices are analysed by transient optoelectronic techniques of carrier kinetics and densities, air photoemission spectroscopy of material energetics, Kelvin probe measurements of work function, Mott-Schottky analyses of apparent doping density and by device modelling. We conclude that, for the device series studied, the photocurrent loss with thick active layers is primarily associated with the accumulation of photo-generated charge carriers in intra-bandgap tail states. This charge accumulation screens the device internal electrical field, preventing efficient charge collection. Purification of one studied donor polymer is observed to reduce tail state distribution and density and increase the maximal photoactive thickness for efficient operation. Our work suggests that selecting organic photoactive layers with a narrow distribution of tail states is a key requirement for the fabrication of efficient, high photocurrent, thick organic solar cells

Proximal Splitting Derivatives for Risk Estimation

This paper develops a novel framework to compute a projected Generalized Stein Unbiased Risk Estimator (GSURE) for a wide class of sparsely regularized solutions of inverse problems. This class includes arbitrary convex data fidelities with both analysis and synthesis mixed L1-L2 norms. The GSURE necessitates to compute the (weak) derivative of a solution w.r.t.~the observations. However, as the solution is not available in analytical form but rather through iterative schemes such as proximal splitting, we propose to iteratively compute the GSURE by differentiating the sequence of iterates. This provides us with a sequence of differential mappings, which, hopefully, converge to the desired derivative and allows to compute the GSURE. We illustrate this approach on total variation regularization with Gaussian noise and to sparse regularization with poisson noise, to automatically select the regularization parameter.Adaptivité pour la représentation des images naturelles et des texturesERC SIGMA-Visio