Germana Landi

537 total citations
49 papers, 374 citations indexed

About

Germana Landi is a scholar working on Computer Vision and Pattern Recognition, Mathematical Physics and Computational Mechanics. According to data from OpenAlex, Germana Landi has authored 49 papers receiving a total of 374 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 19 papers in Mathematical Physics and 16 papers in Computational Mechanics. Recurrent topics in Germana Landi's work include Numerical methods in inverse problems (19 papers), Image and Signal Denoising Methods (17 papers) and Sparse and Compressive Sensing Techniques (15 papers). Germana Landi is often cited by papers focused on Numerical methods in inverse problems (19 papers), Image and Signal Denoising Methods (17 papers) and Sparse and Compressive Sensing Techniques (15 papers). Germana Landi collaborates with scholars based in Italy, United States and Ireland. Germana Landi's co-authors include Elena Loli Piccolomini, Fabiana Zama, Villiam Bortolotti, Daniela Calvetti, Lothar Reichel, Fiorella Sgallari, Paolo G. Morselli, R. Morelli, Antonellá Tosti and Paola Fantazzini and has published in prestigious journals such as Microporous and Mesoporous Materials, Applied Mathematics and Computation and SIAM Journal on Scientific Computing.

In The Last Decade

Germana Landi

43 papers receiving 354 citations

Peers

Germana Landi

CVPR

RNMI

K. Scherer Germany

A.H. Delaney United States

Thomas Goldstein United States

Can Evren Yarman United States

Patrick Joly France

Uno Hämarik Estonia

Jun Xian China

Kamil S. Kazimierski Germany

Fritz Keinert United States

Silvia Gazzola United Kingdom

K. Scherer Germany

Germana Landi

46 ×0.3

CVPR

58 ×0.5

115 ×1.2

RNMI

230 ×2.9

40 ×0.5

Citations per year, relative to Germana Landi Germana Landi (= 1×) peers K. Scherer

Countries citing papers authored by Germana Landi

Since Specialization

Citations

This map shows the geographic impact of Germana Landi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Germana Landi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Germana Landi more than expected).

Fields of papers citing papers by Germana Landi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Germana Landi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Germana Landi. The network helps show where Germana Landi may publish in the future.

Co-authorship network of co-authors of Germana Landi

This figure shows the co-authorship network connecting the top 25 collaborators of Germana Landi. A scholar is included among the top collaborators of Germana Landi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Germana Landi. Germana Landi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Landi, Germana, Marco Viola, & Fabiana Zama. (2025). A Scaled Gradient Projection method for the realization of the balancing principle in TGV-based image restoration. Computational Optimization and Applications. 91(2). 759–785.

Borgatti, Lisa, Davide Donati, Liwei Hu, Germana Landi, & Fabiana Zama. (2025). Estimating landslide thickness through domain-based regularization. Journal of Computational and Applied Mathematics. 476. 117073–117073.

Bortolotti, Villiam, Germana Landi, & Fabiana Zama. (2025). Uniform Multipenalty Regularization for Linear Ill-Posed Inverse Problems. SIAM Journal on Scientific Computing. 47(2). A790–A810. 1 indexed citations

Borgatti, Lisa, Davide Donati, Liwei Hu, Germana Landi, & Fabiana Zama. (2024). A Regularization Method for Landslide Thickness Estimation. Journal of Imaging. 10(12). 314–314. 2 indexed citations

Bortolotti, Villiam, et al.. (2024). Robust Algorithms for the Analysis of Fast-Field-Cycling Nuclear Magnetic Resonance Dispersion Curves. Computers. 13(6). 129–129. 1 indexed citations

Bianchi, S., et al.. (2024). A Network-Based Study of the Dynamics of Aβ and τ Proteins in Alzheimer’s Disease. Mathematical and Computational Applications. 29(6). 113–113.

Bortolotti, Villiam, Germana Landi, & Fabiana Zama. (2023). An Automatic Pixel-Wise Multi-Penalty Approach to Image Restoration. Journal of Imaging. 9(11). 249–249. 2 indexed citations

Landi, Germana, et al.. (2023). An automatic L1-based regularization method for the analysis of FFC dispersion profiles with quadrupolar peaks. Applied Mathematics and Computation. 444. 127809–127809. 5 indexed citations

Bortolotti, Villiam, et al.. (2022). MUPen2DTool: A new Matlab Tool for 2D Nuclear Magnetic Resonance relaxation data inversion. SoftwareX. 20. 101240–101240. 3 indexed citations

10.

Landi, Germana, Fabiana Zama, & Villiam Bortolotti. (2021). A New Hybrid Inversion Method for 2D Nuclear Magnetic Resonance Combining TSVD and Tikhonov Regularization. Journal of Imaging. 7(2). 18–18. 3 indexed citations

11.

Bortolotti, Villiam, Germana Landi, & Fabiana Zama. (2021). 2DNMR data inversion using locally adapted multi-penalty regularization. Computational Geosciences. 25(3). 1215–1228. 8 indexed citations

12.

Landi, Germana, Elena Loli Piccolomini, & James G. Nagy. (2019). Nonlinear conjugate gradient method for spectral tomosynthesis. Inverse Problems. 35(9). 94003–94003. 3 indexed citations

13.

Landi, Germana, Elena Loli Piccolomini, & James G. Nagy. (2017). A limited memory BFGS method for a nonlinear inverse problem in digital breast tomosynthesis. Inverse Problems. 33(9). 95005–95005. 8 indexed citations

14.

Serafino, Daniela di, et al.. (2016). On the regularizing behavior of the SDA and SDC gradient methods in the solution of linear ill-posed problems. Journal of Computational and Applied Mathematics. 302. 81–93. 13 indexed citations

15.

Landi, Germana & Elena Loli Piccolomini. (2011). An improved Newton projection method for nonnegative deblurring of Poisson-corrupted images with Tikhonov regularization. Numerical Algorithms. 60(1). 169–188. 19 indexed citations

16.

Landi, Germana & Elena Loli Piccolomini. (2011). An efficient method for nonnegatively constrained Total Variation-based denoising of medical images corrupted by Poisson noise. Computerized Medical Imaging and Graphics. 36(1). 38–46. 34 indexed citations

17.

Landi, Germana & Elena Loli Piccolomini. (2010). An iterative Lagrange method for the regularization of discrete ill-posed inverse problems. Computers & Mathematics with Applications. 60(6). 1723–1738. 4 indexed citations

18.

Landi, Germana. (2007). The Lagrange method for the regularization of discrete ill-posed problems. Computational Optimization and Applications. 39(3). 347–368. 9 indexed citations

19.

Landi, Germana & Elena Loli Piccolomini. (2007). Numerical methods for the reconstruction of dynamic magnetic resonance images. Inverse Problems in Science and Engineering. 15(3). 267–289. 1 indexed citations

20.

Tosti, Antonellá, et al.. (1993). Carcinoma cuniculatum of the Nail Apparatus: Report of Three Cases. Dermatology. 186(3). 217–221. 37 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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