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87 results on '"Verdoliva, L."'

1. SAR image despeckling through convolutional neural networks

Author

Chierchia, G., Cozzolino, D., Poggi, G., and Verdoliva, L.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper we investigate the use of discriminative model learning through Convolutional Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning strategy, hence it does not recover the filtered image, but the speckle component, which is then subtracted from the noisy one. Training is carried out by considering a large multitemporal SAR image and its multilook version, in order to approximate a clean image. Experimental results, both on synthetic and real SAR data, show the method to achieve better performance with respect to state-of-the-art techniques., Comment: Accepted at 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, Texas, July 23-28, 2017

Published
2017

3. Graphene-Like Based-Chemiresistors Inkjet-Printed onto Paper Substrate

Author

Villani, F., Loffredo, F., Alfano, Brigida, Miglietta, Maria Lucia, Verdoliva, L., Alfè, M., Gargiulo, V., Polichetti, Tiziana, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Andò, Bruno, editor, Baldini, Francesco, editor, Di Natale, Corrado, editor, Ferrari, Vittorio, editor, Marletta, Vincenzo, editor, Marrazza, Giovanna, editor, Militello, Valeria, editor, Miolo, Giorgia, editor, Rossi, Marco, editor, Scalise, Lorenzo, editor, and Siciliano, Pietro, editor

Published
2019
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14. An unsupervised segmentation-based coder for multispectral images

Author

Marco Cagnazzo, Cicala, L., Poggi, G., Scarpa, G., Verdoliva, L., M., Cagnazzo, L., Cicala, Poggi, Giovanni, Scarpa, Giuseppe, and Verdoliva, Luisa

Subjects
Multispectral image coding, Region-based coding, On-board implementation
Abstract

Publication in the conference proceedings of EUSIPCO, Antalya, Turkey, 2005

Published
2005

36. A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage.

Author

Parrilli, S., Poderico, M., Angelino, C. V., and Verdoliva, L.

Subjects
WIENER filters (Signal processing), MEAN square algorithms, SPECKLE interference, SYNTHETIC aperture radar, DISCRETE wavelet transforms
Abstract

We propose a novel despeckling algorithm for synthetic aperture radar (SAR) images based on the concepts of nonlocal filtering and wavelet-domain shrinkage. It follows the structure of the block-matching 3-D algorithm, recently proposed for additive white Gaussian noise denoising, but modifies its major processing steps in order to take into account the peculiarities of SAR images. A probabilistic similarity measure is used for the block-matching step, while the wavelet shrinkage is developed using an additive signal-dependent noise model and looking for the optimum local linear minimum-mean-square-error estimator in the wavelet domain. The proposed technique compares favorably w.r.t. several state-of-the-art reference techniques, with better results both in terms of signal-to-noise ratio (on simulated speckled images) and of perceived image quality. [ABSTRACT FROM AUTHOR]

Published
2012
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37. Region based compression of multispectral images by classified KLT

Author

Marco Cagnazzo, Gaetano, R., Parrilli, S., Verdoliva, L., Cagnazzo, Marco, Gaetano, Raffaele, Parrilli, Sara, and Verdoliva, Luisa

Subjects
remote sensing, image compression, KLT
Abstract

A new region-based algorithm is proposed for the compression of multispectral images. The image is segmented in homogeneous regions, each of which is subject to spectral KLT, spatial shape-adaptive DWT, and SPIHT encoding. We propose to use a dedicated KLT for each region or for each class rather than a single global KLT. Experiments show that the classified KLT guarantees a significant increase in energy compaction, and hence, despite the need to transmit more side information, it provides a valuable performance gain over reference techniques.

48. Facing Device Attribution Problem for Stabilized Video Sequences

Author

Stefano Tubaro, Sara Mandelli, Paolo Bestagini, Luisa Verdoliva, Mandelli, S., Bestagini, P., Verdoliva, L., and Tubaro, S.

Subjects
device attribution, device fingerprint estimation, photo response non-uniformity (PRNU), Video motion stabilization, FOS: Computer and information sciences, 021110 strategic, defence & security studies, Computer Networks and Communications, Computer science, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, Video sequence, 02 engineering and technology, Multimedia (cs.MM), Image stabilization, Robustness (computer science), Computer vision, Artificial intelligence, Image sensor, Safety, Risk, Reliability and Quality, business, Computer Science - Multimedia
Abstract

A problem deeply investigated by multimedia forensics researchers is the one of detecting which device has been used to capture a video. This enables to trace down the owner of a video sequence, which proves extremely helpful to solve copyright infringement cases as well as to fight distribution of illicit material (e.g., underage clips, terroristic threats, etc.). Currently, the most promising methods to tackle this task exploit unique noise traces left by camera sensors on acquired images. However, given the recent advancements in motion stabilization of video content, robustness of sensor pattern noise-based techniques are strongly hindered. Indeed, video stabilization introduces geometric transformations between video frames, thus making camera fingerprint estimation problematic with classical approaches. In this paper, we deal with the challenging problem of attributing stabilized videos to their recording device. Specifically, we propose: (i) a strategy to extract the characteristic fingerprint of a device, starting from either a set of images or stabilized video sequences; (ii) a strategy to match a stabilized video sequence with a given fingerprint in order to solve the device attribution problem. The proposed methodology is tested on videos coming from a set of different smartphones, taken from the modern publicly available Vision Dataset. The conducted experiments also provide an interesting insight on the effect of modern smartphones video stabilization algorithms on specific video frames.

Published
2020
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49. Stability assessment of liquid formulations: A deep learning approach

Author

Maurizio De Micco, Diego Gragnaniello, Fabio Zonfrilli, Vincenzo Guida, Massimiliano M. Villone, Giovanni Poggi, Luisa Verdoliva, De Micco, M., Gragnaniello, D., Zonfrilli, F., Guida, V., Villone, M. M., Poggi, G., and Verdoliva, L.

Subjects
Applied Mathematics, General Chemical Engineering, General Chemistry, Industrial and Manufacturing Engineering
Published
2022
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50. Guided Patchwise Nonlocal SAR Despeckling

Author

Giovanni Poggi, Luisa Verdoliva, Sergio Vitale, Davide Cozzolino, Giuseppe Scarpa, Vitale, S., Cozzolino, D., Scarpa, G., Verdoliva, L., and Poggi, G.

Subjects
FOS: Computer and information sciences, Synthetic aperture radar, Computer science, business.industry, Computer Vision and Pattern Recognition (cs.CV), nonlocal filtering, Computer Science - Computer Vision and Pattern Recognition, 0211 other engineering and technologies, 02 engineering and technology, Filter (signal processing), Image (mathematics), Despeckling, Speckle pattern, SAR-optical fusion, General Earth and Planetary Sciences, Computer vision, Artificial intelligence, Electrical and Electronic Engineering, business, 021101 geological & geomatics engineering
Abstract

We propose a new method for synthetic aperture radar (SAR) image despeckling, which leverages information drawn from coregistered optical imagery. Filtering is performed by patchwise nonlocal means, working exclusively on SAR data. However, the filtering weights are computed by taking into account also the optical guide, which is much cleaner than the SAR image, and hence more discriminative. To avoid injecting optical-domain information into the filtered image, an SAR-domain statistical test is preliminarily performed to reject right away any risky predictor. Experiments on two SAR-optical data sets prove the proposed method to suppress very effectively the speckle, preserving structural details, and without introducing significant filtering artifacts. Overall, the proposed method compares favorably with all the state-of-the-art despeckling filters, and also with our own previous optical-guided filter.

Published
2019
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