1,721,085 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Erratum: Efficient gradient projection methods for edge-preserving removal of Poisson noise (Inverse Problems (2009) 25 (045010))
No full textThis file contains a complete proof of lemma 1 of the paper
"Efficient gradient projection methods
for edge-preserving removal of Poisson noise",
2009 Inverse Problems 25 045010
Special Issue on "Optimization Methods for Inverse Problems in Imaging": Guest Editorial
No full textThe aim of this special issue is to focus on the growing interaction between inverse
problems in imaging science and optimization, that in recent years has given rise to
significant advances in both the areas: optimization-based tools have been developed
to solve challenging image reconstruction problems while the experience with imaging
problems has led to an improved and deeper understanding of certain optimization
algorithms. The issue includes 10 peer reviewed papers whose contributions represent
new advances in numerical optimization for inverse problems with significant
impact in signal and image processing
Nonnegative least-squares image deblurring: improved gradient projection approaches
No full textThe least-squares approach to image deblurring leads to an ill-posed problem. The addition of the nonnegativity constraint, when appropriate, does not provide regularization, even if, as far as we know, a thorough investigation of the ill-posedness of the resulting constrained least-squares problem has still to be done. Iterative methods, converging to nonnegative least-squares solutions, have been proposed. Some of them have the "semi-convergence'' property, i.e. early stopping of the iteration provides "regularized'' solutions. In this paper we consider two of these methods: the projected Landweber (PL) method and the iterative image space reconstruction algorithm (ISRA).Even if they work well in many instances, they are not frequently used in practice because, in general, they require a large number of iterations before providing a sensible solution. Therefore the main purpose of this paper is to refresh these methods by increasing their efficiency. Starting from the remark that PL and ISRA require only the computation of the gradient of the functional, we propose the application to these algorithms of special acceleration techniques that have been recently developed in the area of the gradient methods. In particular, we propose the application of efficient step-length selection rulesand line-search strategies. Moreover, remarking that ISRA is a scaled gradient algorithm, we evaluate its behaviour in comparison with a recent scaled gradient projection (SGP) method for image deblurring. Numerical experiments demonstrate that the accelerated methods still exhibit thesemi-convergence property, with a considerable gain both in the number of iterations and in the computational time; in particular, SGP appears definitely the most efficient one
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