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5. Cancelable templates for secure face verification based on deep learning and random projections.

Author

Ali, Arslan, Migliorati, Andrea, Bianchi, Tiziano, and Magli, Enrico

Subjects
DEEP learning, RANDOM matrices, HUMAN fingerprints, DATA protection, DESIGN templates, BIOMETRIC identification, BIOMETRY
Abstract

Recently, biometric recognition has become a significant field of research. The concept of cancelable biometrics (CB) has been introduced to address security concerns related to the handling of sensitive data. In this paper, we address unconstrained face verification by proposing a deep cancelable framework called BiometricNet+ that employs random projections (RP) to conceal face images and compressive sensing (CS) to reconstruct measurements in the original domain. Our lightweight design enforces the properties of unlinkability, revocability, and non-invertibility of the templates while preserving face recognition accuracy. We compare facial features by learning a regularized metric: at training time, we jointly learn facial features and the metric such that matching and non-matching pairs are mapped onto latent target distributions; then, for biometric verification, features are randomly projected via random matrices changed at every enrollment and query and reconstructed before the latent space mapping is computed. We assess the face recognition accuracy of our framework on challenging datasets such as LFW, CALFW, CPLFW, AgeDB, YTF, CFP, and RFW, showing notable improvements over state-of-the-art techniques while meeting the criteria for secure cancelable template design. Since our method requires no fine-tuning of the learned features, it can be applied to pre-trained networks to increase sensitive data protection. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Fast Inference in Denoising Diffusion Models via MMD Finetuning

Author

Aiello, Emanuele, Valsesia, Diego, and Magli, Enrico

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Denoising Diffusion Models (DDMs) have become a popular tool for generating high-quality samples from complex data distributions. These models are able to capture sophisticated patterns and structures in the data, and can generate samples that are highly diverse and representative of the underlying distribution. However, one of the main limitations of diffusion models is the complexity of sample generation, since a large number of inference timesteps is required to faithfully capture the data distribution. In this paper, we present MMD-DDM, a novel method for fast sampling of diffusion models. Our approach is based on the idea of using the Maximum Mean Discrepancy (MMD) to finetune the learned distribution with a given budget of timesteps. This allows the finetuned model to significantly improve the speed-quality trade-off, by substantially increasing fidelity in inference regimes with few steps or, equivalently, by reducing the required number of steps to reach a target fidelity, thus paving the way for a more practical adoption of diffusion models in a wide range of applications. We evaluate our approach on unconditional image generation with extensive experiments across the CIFAR-10, CelebA, ImageNet and LSUN-Church datasets. Our findings show that the proposed method is able to produce high-quality samples in a fraction of the time required by widely-used diffusion models, and outperforms state-of-the-art techniques for accelerated sampling. Code is available at: https://github.com/diegovalsesia/MMD-DDM.

Published
2023

12. Rethinking the compositionality of point clouds through regularization in the hyperbolic space

Author

Montanaro, Antonio, Valsesia, Diego, and Magli, Enrico

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Point clouds of 3D objects exhibit an inherent compositional nature where simple parts can be assembled into progressively more complex shapes to form whole objects. Explicitly capturing such part-whole hierarchy is a long-sought objective in order to build effective models, but its tree-like nature has made the task elusive. In this paper, we propose to embed the features of a point cloud classifier into the hyperbolic space and explicitly regularize the space to account for the part-whole hierarchy. The hyperbolic space is the only space that can successfully embed the tree-like nature of the hierarchy. This leads to substantial improvements in the performance of state-of-art supervised models for point cloud classification., NeurIPS 2022

Published
2022

13. Cross-modal Learning for Image-Guided Point Cloud Shape Completion

Author

Aiello, Emanuele, Valsesia, Diego, and Magli, Enrico

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

In this paper we explore the recent topic of point cloud completion, guided by an auxiliary image. We show how it is possible to effectively combine the information from the two modalities in a localized latent space, thus avoiding the need for complex point cloud reconstruction methods from single views used by the state-of-the-art. We also investigate a novel weakly-supervised setting where the auxiliary image provides a supervisory signal to the training process by using a differentiable renderer on the completed point cloud to measure fidelity in the image space. Experiments show significant improvements over state-of-the-art supervised methods for both unimodal and multimodal completion. We also show the effectiveness of the weakly-supervised approach which outperforms a number of supervised methods and is competitive with the latest supervised models only exploiting point cloud information., NeurIPS 2022

Published
2022

14. The New CCSDS Standard for Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression

Author

Kiely, Aaron B, Klimesh, Matthew, Blanes, Ian, Ligo, Jonathan, Magli, Enrico, Aranki, Nazeeh, Burl, Michael, Camarero, Roberto, Cheng, Michael, Dolinar, Sam, Dolman, David, Flesch, Greg, Ghassemi, Hamid, Gilbert, Martin, Hernández-Cabronero, Miguel, Keymeulen, Didier, Le, Martin, Luong, Huy, McGuinness, Christopher, Moury, Gilles, Pham, Thang, Plintovic, Martin, Sala, Frederic, Santos, Lucana, Schaar, Alan, Serra-Sagrist, Joan, Shin, Simon, Sundlie, Brenton, Valsesia, Diego, Vitulli, Raffaele, Wong, Englin, Wu, William, Xie, Hua, and Zhou, Hanying

Subjects
Computer Programming And Software
Abstract

This paper describes the emerging Issue 2 of the CCSDS-123.0-B standard for low-complexity compression of multispectral and hyperspectral imagery, focusing on its new features and capabilities. Most significantly, this new issue incorporates a closed-loop quantization scheme to provide near-lossless compression capability while still supporting lossless compression, and introduces a new entropy coding option that provides better compression of low-entropy data.

Published
2018

17. Super-Resolved Multi-Temporal Segmentation with Deep Permutation-Invariant Networks

Author

Valsesia, Diego and Magli, Enrico

Subjects
FOS: Computer and information sciences, deep neural networks, Computer Vision and Pattern Recognition (cs.CV), Super-resolution, Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing, image segmentation
Abstract

Multi-image super-resolution from multi-temporal satellite acquisitions of a scene has recently enjoyed great success thanks to new deep learning models. In this paper, we go beyond classic image reconstruction at a higher resolution by studying a super-resolved inference problem, namely semantic segmentation at a spatial resolution higher than the one of sensing platform. We expand upon recently proposed models exploiting temporal permutation invariance with a multi-resolution fusion module able to infer the rich semantic information needed by the segmentation task. The model presented in this paper has recently won the AI4EO challenge on Enhanced Sentinel 2 Agriculture., Comment: IGARSS 2022

Published
2022

18. Exploring the Solution Space of Linear Inverse Problems with GAN Latent Geometry

Author

Montanaro, Antonio, Valsesia, Diego, and Magli, Enrico

Subjects
FOS: Computer and information sciences, Inverse problems, GANs, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Inverse problems consist in reconstructing signals from incomplete sets of measurements and their performance is highly dependent on the quality of the prior knowledge encoded via regularization. While traditional approaches focus on obtaining a unique solution, an emerging trend considers exploring multiple feasibile solutions. In this paper, we propose a method to generate multiple reconstructions that fit both the measurements and a data-driven prior learned by a generative adversarial network. In particular, we show that, starting from an initial solution, it is possible to find directions in the latent space of the generative model that are null to the forward operator, and thus keep consistency with the measurements, while inducing significant perceptual change. Our exploration approach allows to generate multiple solutions to the inverse problem an order of magnitude faster than existing approaches; we show results on image super-resolution and inpainting problems., Comment: ICIP 2022

Published
2022

19. Compressive Sensing Imaging Spectrometer for UV-Vis Stellar Spectroscopy: Instrumental Concept and Performance Analysis.

Author

Nardino, Vanni, Guzzi, Donatella, Lastri, Cinzia, Palombi, Lorenzo, Coluccia, Giulio, Magli, Enrico, Labate, Demetrio, and Raimondi, Valentina

Subjects
ULTRAVIOLET-visible spectroscopy, SPECTROMETERS, MEDICAL microscopy, SPATIAL light modulators
Abstract

Compressive sensing (CS) has been proposed as a disruptive approach to developing a novel class of optical instrumentation used in diverse application domains. Thanks to sparsity as an inherent feature of many natural signals, CS allows for the acquisition of the signal in a very compact way, merging acquisition and compression in a single step and, furthermore, offering the capability of using a limited number of detector elements to obtain a reconstructed image with a larger number of pixels. Although the CS paradigm has already been applied in several application domains, from medical diagnostics to microscopy, studies related to space applications are very limited. In this paper, we present and discuss the instrumental concept, optical design, and performances of a CS imaging spectrometer for ultraviolet-visible (UV–Vis) stellar spectroscopy. The instrument—which is pixel-limited in the entire 300 nm–650 nm spectral range—features spectral sampling that ranges from 2.2 nm@300 nm to 22 nm@650 nm, with a total of 50 samples for each spectrum. For data reconstruction quality, the results showed good performance, measured by several quality metrics chosen from those recommended by CCSDS. The designed instrument can achieve compression ratios of 20 or higher without a significant loss of information. A pros and cons analysis of the CS approach is finally carried out, highlighting main differences with respect to a traditional system. [ABSTRACT FROM AUTHOR]

Published
2023
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20. Exploring the Solar Wind from Its Source on the Corona into the Inner Heliosphere during the First Solar Orbiter-Parker Solar Probe Quadrature

Author

Froment, C., Krasnoselskikh, V., Agapitov, O., Fargette, N., Lavraud, B., Larosa, A., Kretzschmar, M., Jagarlamudi, V., Velli, Marco, Malaspina, David, Whittlesey, Phyllis, Bale, Stuart, Case, Anthony, Goetz, Keith, Kasper, Justin, Korreck, Kelly, Larson, Davin, MacDowall, Robert, Mozer, F., Pulupa, Marc, Revillet, C., Stevens, Michael, Telloni, Daniele, Andretta, Vincenzo, Antonucci, Ester, Bemporad, Alessandro, Capuano, Giuseppe, Fineschi, Silvano, Giordano, Silvio, Habbal, Shadia, Perrone, Denise, Pinto, Rui, Sorriso-Valvo, Luca, Spadaro, Daniele, Susino, Roberto, Woodham, Lloyd, Zank, Gary, Romoli, Marco, Auchère, Frédéric, Bruno, Roberto, Capobianco, Gerardo, Casini, Chiara, Casti, Marta, Chioetto, Paolo, Corso, Alain, Da Deppo, Vania, De Leo, Yara, Dudok De Wit, Thierry, Frassati, Federica, Frassetto, Fabio, Guglielmino, Salvo, Harvey, Peter, Heinzel, Petr, Jerse, Giovanna, Landini, Federico, Liberatore, Alessandro, Livi, Roberto, Magli, Enrico, Massone, Giuseppe, Messerotti, Mauro, Moses, John, Naletto, Giampiero, Nicolini, Gianalfredo, Nisticò, Giuseppe, Panasenco, Olga, Pancrazzi, Maurizio, Pelizzo, Maria, Reale, Fabio, Romano, Paolo, Sasso, Clementina, Schühle, Udo, Stangalini, Marco, Strachan, Leonard, Straus, Thomas, Teriaca, Luca, Uslenghi, Michela, Verscharen, Daniel, Volpicelli, Cosimo, Zangrilli, Luca, Zimbardo, Gaetano, Zuppella, Paola, Telloni, Daniele, Andretta, Vincenzo, Antonucci, Ester, Bemporad, Alessandro, Capuano, Giuseppe E., Fineschi, Silvano, Giordano, Silvio, Habbal, Shadia, Perrone, Denise, Pinto, Rui F., Sorriso-Valvo, Luca, Spadaro, Daniele, Susino, Roberto, Woodham, Lloyd D., Zank, Gary P., Romoli, Marco, Bale, Stuart D., Kasper, Justin C., Auchère, Frédéric, Bruno, Roberto, Capobianco, Gerardo, Case, Anthony W., Casini, Chiara, Casti, Marta, Chioetto, Paolo, Corso, Alain J., Da Deppo, Vania, De Leo, Yara, Dudok de Wit, Thierry, Frassati, Federica, Frassetto, Fabio, Goetz, Keith, Guglielmino, Salvo L., Harvey, Peter R., Heinzel, Petr, Jerse, Giovanna, Korreck, Kelly E., Landini, Federico, Larson, Davin, Liberatore, Alessandro, Livi, Roberto, MacDowall, Robert J., Magli, Enrico, Malaspina, David M., Massone, Giuseppe, Messerotti, Mauro, Moses, John D., Naletto, Giampiero, Nicolini, Gianalfredo, Nisticò, Giuseppe, Panasenco, Olga, Pancrazzi, Maurizio, Pelizzo, Maria G., Pulupa, Marc, Reale, Fabio, Romano, Paolo, Sasso, Clementina, Schühle, Udo, Stangalini, Marco, Stevens, Michael L., Strachan, Leonard, Straus, Thoma, Teriaca, Luca, Uslenghi, Michela, Velli, Marco, Verscharen, Daniel, Volpicelli, Cosimo A., Whittlesey, Phylli, Zangrilli, Luca, Zimbardo, Gaetano, Zuppella, Paola, Laboratoire de Physique et Chimie de l'Environnement et de l'Espace (LPC2E), Observatoire des Sciences de l'Univers en région Centre (OSUC), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université d'Orléans (UO)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université d'Orléans (UO)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National d’Études Spatiales [Paris] (CNES), Institut de recherche en astrophysique et planétologie (IRAP), Institut national des sciences de l'Univers (INSU - CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Institut d'astrophysique spatiale (IAS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Centre National d’Études Spatiales [Paris] (CNES)

Subjects
[PHYS.ASTR.IM]Physics [physics]/Astrophysics [astro-ph]/Instrumentation and Methods for Astrophysic [astro-ph.IM], Astrophysics::High Energy Astrophysical Phenomena, Solar wind, FOS: Physical sciences, Solar radius, Solar corona, Astrophysics, 01 natural sciences, law.invention, Current sheet, Orbiter, Magnetohydrodynamics, Interplanetary turbulence, Heliosphere, Physics - Space Physics, [PHYS.PHYS.PHYS-PLASM-PH]Physics [physics]/Physics [physics]/Plasma Physics [physics.plasm-ph], law, 0103 physical sciences, Astrophysics::Solar and Stellar Astrophysics, 010303 astronomy & astrophysics, Coronagraph, Solar and Stellar Astrophysics (astro-ph.SR), Physics, [SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph], 010308 nuclear & particles physics, Space plasmas, Astronomy and Astrophysics, Plasma, [PHYS.ASTR.SR]Physics [physics]/Astrophysics [astro-ph]/Solar and Stellar Astrophysics [astro-ph.SR], Corona, Space Physics (physics.space-ph), [PHYS.PHYS.PHYS-SPACE-PH]Physics [physics]/Physics [physics]/Space Physics [physics.space-ph], Physics - Plasma Physics, Plasma Physics (physics.plasm-ph), Astrophysics - Solar and Stellar Astrophysics, Space and Planetary Science, Physics::Space Physics, Astrophysics::Earth and Planetary Astrophysics, [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an]
Abstract

This Letter addresses the first Solar Orbiter (SO) -- Parker Solar Probe (PSP) quadrature, occurring on January 18, 2021, to investigate the evolution of solar wind from the extended corona to the inner heliosphere. Assuming ballistic propagation, the same plasma volume observed remotely in corona at altitudes between 3.5 and 6.3 solar radii above the solar limb with the Metis coronagraph on SO can be tracked to PSP, orbiting at 0.1 au, thus allowing the local properties of the solar wind to be linked to the coronal source region from where it originated. Thanks to the close approach of PSP to the Sun and the simultaneous Metis observation of the solar corona, the flow-aligned magnetic field and the bulk kinetic energy flux density can be empirically inferred along the coronal current sheet with an unprecedented accuracy, allowing in particular estimation of the Alfv\'en radius at 8.7 solar radii during the time of this event. This is thus the very first study of the same solar wind plasma as it expands from the sub-Alfv\'enic solar corona to just above the Alfv\'en surface., Comment: 10 pages, 4 figures

Published
2021

22. A Novel Architecture for the Next Generation of Earth Observation Satellites Supporting Rapid Civil Alert

Author

Kerr, Murray, Tonetti, Stefania, Cornara, Stefania, Bravo, Juan-Ignacio, Hinz, Robert, Latorre, Antonio, Membibre, Francisco, Ramos, Alexis, Moron, Alvaro, Solimini, Chiara, Wiehle, Stefan, Breit, Helko, Günzel, Dominik, Mandapati, Srikantha, Tings, Björn, Balss, Ulrich, Koudelka, Otto, Teschl, Franz, Magli, Enrico, Bianchi, Tiziano, Migliorati, Andrea, Motto Ros, Paolo, Caon, Michele, Martina, Maurizio, Freddi, Riccardo, Milani, Fabio, Curci, Guido, Fraile, Silvia, and Marcos, Cecilia

Subjects
civil alert, ship detection, earth observation, Synthetic Aperture Radar, FPGA, SAR, onboard processing
Published
2021

23. EO-ALERT: an on-board data processing and handling architecture for a rapid alerts service

Author

Kerr, Murray, Tonetti, Stefania, Cornara, Stefania, Bravo, Juan-Ignacio, Hinz, Robert, Latorre, Antonio, Membibre, Francisco, Ramos, Alexis, Wiehle, Stefan, Koudelka, Otto, Magli, Enrico, Freddi, Riccardo, Fraile, Silvia, and Marcos, Cecilia

Subjects
civil alert, ship detection, earth observation, Synthetic Aperture Radar, FPGA, SAR, onboard processing
Published
2021

25. EO-Alert: A Novel Satellite Architecture for the Next Generation of Earth Observation Satellites Supporting Rapid Alerts

Author

Kerr, Murray, Tonetti, Stefania, Cornara, Stefania, Bravo, Juan-Ignacio, Hinz, Robert, Latorre, Antonio, Membibre, Francisco, Ramos, Alexis, Solimini, Chiara, Wiehle, Stefan, Breit, Helko, Günzel, Dominik, Mandapati, Srikantha, Tings, Björn, Balss, Ulrich, Koudelka, Otto, Teschl, Franz, Magli, Enrico, Bianchi, Tiziano, Migliorati, Andrea, Motto Ros, Paolo, Caon, Michele, Martina, Maurizio, Freddi, Riccardo, Milani, Fabio, Curci, Guido, Fraile, Silvia, and Marcos, Cecilia

Subjects
civil alert, ship detection, earth observation, Synthetic Aperture Radar, FPGA, SAR, onboard processing
Published
2021

27. Distributed arithmetic coding for the slepian-wolf probelm

Author

Grangetto, Marco, Magli, Enrico, and Olmo, Gabriella

Subjects
Arithmetic functions -- Usage, Distributed processing (Computers) -- Analysis, Entropy (Information theory) -- Analysis, Signal processing -- Research, Distributed processing (Computers), Digital signal processor, Business, Computers, Electronics, Electronics and electrical industries
Published
2009

28. Multiband lossless compression of hyperspectral images

Author

Magli, Enrico

Subjects
Data compression -- Analysis, Image processing -- Analysis, Kalman filtering -- Analysis, Business, Earth sciences, Electronics and electrical industries
Abstract

Hyperspectral images exhibit significant spectral correlation, whose exploitation is crucial for compression. In this paper, we investigate the problem of predicting a given band of a hyperspectral image using more than one previous band. We present an information-theoretic analysis based on the concept of conditional entropy, which is used to assess the available amount of correlation and the potential compression gain. Then, we propose a new lossless compression algorithm that employs a Kalman filter in the prediction stage. Simulation results are presented on Airborne Visible Infrared Imaging Spectrometer, Hyperspectral Digital Imagery Collection Experiment, and Hyperspectrai Mapper scenes, showing competitive performance with other state-of-the-art compression algorithms. Index Terms--Conditional entropy, hyperspectral data, Kalman filter, lossless compression, 3-D prediction.

Published
2009

30. The SURPRISE demonstrator: a super-resolved compressive instrument in the visible and medium infrared for Earth Observation

Author

Valentina, Raimondi, Luigi, Acampora, Gabriele, Amato, Massimo, Baldi, Dirk, Berndt, Alberto, Bianchi, Bianchi, Tiziano, Valentina, Colcelli, Chiara, Corti, Francesco, Corti, Marco, Corti, Nick, Cox, Dauderstädt, Ulrike A., Peter, Dürr, Sara Francés González, Paolo, Frosini, Donatella, Guzzi, Jessica, Huntingford, Demetrio, Labate, Nicolas, Lamquin, Cinzia, Lastri, Magli, Enrico, Vanni, Nardino, Lorenzo, Palombi, Irene, Pettinelli, Giuseppe, Pilato, Alexandre, Pollini, Leopoldo, Rossini, Enrico, Suetta, Taricco, Davide, Valsesia, Diego, and Michael, Wagner

Published
2020

32. Transform coding techniques for lossy hyperspectral data compression

Author

Penna, Barbara, Tillo, Tammam, Magli, Enrico, and Olmo, Gabriella

Subjects
Wavelet transforms -- Analysis, Business, Earth sciences, Electronics and electrical industries
Abstract

Transform-based lossy compression has a huge potential for hyperspectral data reduction. Hyperspectral data are 3-D, and the nature of their correlation is different in each dimension. This calls for a careful design of the 3-D transform to be used for compression. In this paper, we investigate the transform design and rate allocation stage for lossy compression of hyperspectral data. First, we select a set of 3-D transforms, obtained by combining in various ways wavelets, wavelet packets, the discrete cosine transform, and the Karhunen-Loeve transform (KLT), and evaluate the coding efficiency of these combinations. Second, we propose a low-complexity version of the KLT, in which complexity and performance can be balanced in a scalable way, allowing one to design the transform that better matches a specific application. Third, we integrate this, as well as other existing transforms, in the framework of Part 2 of the Joint Photographic Experts Group (JPEG) 2000 standard, taking advantage of the high coding efficiency of JPEG 2000, and exploiting the interoperability of an international standard. We introduce an evaluation framework based on both reconstruction fidelity and impact on image exploitation, and evaluate the proposed algorithm by applying this framework to AVIRIS scenes. It is shown that the scheme based on the proposed low-complexity KLT significantly outperforms previous schemes as to rate-distortion performance. As for impact on exploitation, we consider multiclass hard classification, spectral unmixing, binary classification, and anomaly detection as benchmark applications. Index Terms--Anomaly detection, classification, discrete cosine transform (DCT), hyperspectral data, Joint Photographic Experts Group (JPEG) 2000, Karhunen-Loeve transform (KLT), lossy compression, spectral unmixing, wavelets.

Published
2007

33. A syntax-preserving error resilience tool for JPEG 2000 based on error correcting arithmetic coding

Author

Grangetto, Marco, Magli, Enrico, and Olmo, Gabriella

Subjects
Image coding -- Standards, Mobile communication systems -- Standards, Wireless communication systems -- Standards, Wireless technology, Business, Computers, Electronics, Electronics and electrical industries
Abstract

A novel error-resilience tool for JPEG 2000 is proposed, based on the concept of ternary arithmetic coders employing a forbidden symbol. The proposed decoder extends the JPEG 2000 capabilities in error-prone scenarios, without violating the standard syntax, and extensive simulations on video sequences show that the proposed decoders largely outperform the standard in terms of PSNR and visual quality.

Published
2006

36. Fast code-rate optimization for robust image transmission over lossy packet networks

Author

Grangetto, Marco, Magli, Enrico, and Olmo, Gabriella

Subjects
Algorithm, Data communications -- Methods, Coding theory -- Methods, Algorithms
Abstract

In this paper, we propose an efficient method for the allocation of Reed-Solomon codes to source symbols, for unequal loss protection. The proposed formulation recasts the multivariate optimization problem into a univariate one, dramatically reducing the computational complexity. Results are shown for image transmission over lossy packet networks, employing the JPEG2000 and SPIHT encoders. The proposed algorithm exhibits performance equivalent to previous methods, while providing a significant complexity reduction. Index Terms--JPEG2000, multiple description coding, Reed-Solomon codes, SPIHT, unequal loss protection.

Published
2005

37. Ensuring quality of service for image transmission: hybrid loss protection

Author

Grangetto, Marco, Magli, Enrico, and Olmo, Gabriella

Subjects
Image coding -- Analysis, Image processing -- Analysis, Quality of service, Business, Computers, Electronics, Electronics and electrical industries
Abstract

Hybrid loss protection as a new channel of coding and packetization scheme for image transmission over nonprioritized lossy packet network is presented. A new code-allocation algorithm is proposed and the results are provided in the case of transmission of imagesencoded by SPIHT and JPEG 2000 over a wireline, as well as a wireless UMTS-based internet connection.

Published
2004

38. Lossy predictive coding of SAR raw data

Author

Magli, Enrico and Olmo, Gabriella

Subjects
Synthetic aperture radar -- Research, Business, Earth sciences, Electronics and electrical industries
Abstract

In this paper, we propose to employ predictive coding for lossy compression of synthetic aperture radar (SAR) raw data. We exploit the known result that a blockwise normalized SAR raw signal is a Gaussian stationary process in order to design an optimal decorrelator for this signal. We show that, due to the statistical properties of the SAR signal, an along-range linear predictor with few taps is able to effectively capture most of the raw signal correlation. The proposed predictive coding algorithm, which performs quantization of the prediction error, optionally followed by entropy coding, exhibits a number of advantages, and notably an interesting performance/complexity trade-off, with respect to other techniques such as flexible block adaptive quantization (FBAQ) or methods based on transform-coding; fractional output bit-rates can also be achieved in the entropy-constrained mode. Simulation results on real-world SIR-C/X-SAR as well as simulated raw and image data show that the proposed algorithm outperforms FBAQ as to SNR, at a computational cost compatible with modern SAR systems. Index Terms--Differential pulse code modulation (DPCM), entropy coding, entropy-constrained quantization, predictive coding, synthetic aperture radar (SAR) raw data compression.

Published
2003

39. Speckle2Void: Deep Self-Supervised SAR Despeckling With Blind-Spot Convolutional Neural Networks.

Author

Molini, Andrea Bordone, Valsesia, Diego, Fracastoro, Giulia, and Magli, Enrico

Subjects
CONVOLUTIONAL neural networks, SYNTHETIC aperture radar, SPECKLE interference, DEEP learning, DATA mining, SELF-efficacy
Abstract

Information extraction from synthetic aperture radar (SAR) images is heavily impaired by speckle noise, and hence, despeckling is a crucial preliminary step in scene analysis algorithms. The recent success of deep learning envisions a new generation of despeckling techniques that could outperform classical model-based methods. However, current deep learning approaches to despeckling require supervision for training, whereas clean SAR images are impossible to obtain. In the literature, this issue is tackled by resorting to either synthetically speckled optical images, which exhibit different properties with respect to true SAR images, or multitemporal SAR images, which are difficult to acquire or fuse accurately. In this article, inspired by recent works on blind-spot denoising networks, we propose a self-supervised Bayesian despeckling method. The proposed method is trained by employing only noisy SAR images and can, therefore, learn features of real SAR images rather than synthetic data. Experiments show that the performance of the proposed approach is very close to the supervised training approach on synthetic data and superior on real data in both quantitative and visual assessments. [ABSTRACT FROM AUTHOR]

Published
2022
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40. Super-resolved compressive demonstrator for Earth Observation applications in the Medium Infrared: instrumental concept, optical design and expected performances.

Author

Raimondi, Valentina, Baldi, Massimo, Berndt, Dirk, Bianchi, Tiziano, Borque Gallego, Guzmán, Borrelli, Donato, Carlà, Roberto, Corti, Chiara, Corti, Francesco, Corti, Marco, Dauderstädt, Ulrike, Dürr, Peter, Gonnelli, Andrea, Francés González, Sara, Guzzi, Donatella, Labate, Demetrio, Lamquin, Nicolas, Lastri, Cinzia, Magli, Enrico, and Marzi, Emiliano

Published
2024
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41. Density evolution of Orthogonal Matching Pursuit

Author

Petit, Claude, Roumy, Aline, Coluccia, Giulio, Magli, Enrico, Institut national de la statistique et des études économiques (INSEE), Analysis representation, compression and communication of visual data (Sirocco), SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Image Processing Lab [Torino] (IPL), Politecnico di Torino = Polytechnic of Turin (Polito), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

42. Spotlight on the Multimedia Signal Processing Technical Committee

Author

Mrak, Marta, Magli, Enrico, Dufaux, Frédéric, BBC Research & Development, BBC, Image Processing Lab [Torino] (IPL), Politecnico di Torino = Polytechnic of Turin (Polito), Laboratoire des signaux et systèmes (L2S), and Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; The Multimedia Signal Processing Technical Committee (MMSP-TC) of the IEEE Signal Processing Society (SPS) promotes the advancement of multimedia signal processing technology. The scope of the TC includes joint processing/representation of audiovisual and multimodal information, fusion/fission of sensor information or multimodal data, integration of media, art and multimedia technology, and analysis and feature extraction of multimodal data. Other key areas encompass virtual reality and 3D imaging, multimedia communications and networking, human-machine interface and interaction, visual and auditory quality assessment, multimedia databases and digital libraries. In this context, the TC also serves as an incubator of technologies that lie in the gaps between traditional areas. Each year, the MMSP-TC organizes the IEEE International Workshop on Multimedia Signal Processing (MMSP), which attracts researchers from SPS and related communities that work on multimedia topics. Running as a single-track event, the workshop consists of oral and poster sessions, including special sessions that capture specific research trends. It also includes invited plenary presentations (in 2018, G. Durity from Electronic Arts, J.-N. Hwang from University of Washington, and H. Malvar from Microsoft Research) and panel discussions (in 2017, on deep learning in multimedia signal processing research). The workshop typically receives around 150 paper submissions and has more than 100 attendees from all over the world. The workshop has been effective at building a community around the thematic of multimedia signal processing, and it enjoys a good reputation among researchers/academics. To further strengthen the event, the TC is also considering means to more effectively engage industry and students. Pictures of the 2017 and 2018 events are shown in Figure 1 and Figure 2. Figure 1: MMSP 2017 in London-Luton, UK.

Published
2019

45. EO-ALERT: Next Generation Satellite Processing Chain for Rapid Civil Alerts

Author

Kerr, Murray, Cornara, Stefania, Latorre, Antonio, Tonetti, Stefania, Fiengo, Aniello, Guardabrazo, Tomas-Alberto, Bravo, Juan-Ignacio, Velotto, Domenico, Eineder, Michael, Jacobsen, Sven, Breit, Helko, Koudelka, O., Teschl, Franz, Magli, Enrico, Bianchi, Tiziano, Tampellini, L., Freddi, R., Fabrizi, Roberto, and Fraile, Silvia

Subjects
EO-ALERT, On-board processing, HW processing, SAR-Signalverarbeitung
Abstract

In this paper, we provide an overview of the H2020 EU project EO-ALERT. The aim of EO-ALERT is to propose the definition and development of the next-generation Earth observation (EO) data and processing chain, based on a novel flight segment architecture moving optimised key EO data processing elements from the ground segment to on-board the satellite. The objective is to address the need for increased throughput in EO data chain, delivering EO products to the end user with very low latency.

Published
2018

47. Error sensitivity data structures and retransmission strategies for robust JPEG 2000 wireless imaging

Author

Grangetto, Marco, Magli, Enrico, and Olmo, Gabriella

Subjects
Image transmission -- Research, Business, Electronics and electrical industries, Engineering and manufacturing industries
Abstract

In this paper we address the problem of JPEG 2000 imaging in a wireless environment. We first define a flexible and efficient data structure for the description of the error sensitivity of different parts of a JPEG 2000 codestream or file format; the data structure is designed in such a way that it can be seamlessly integrated as payload of a JPEG 2000 marker segment or file format box. Moreover, we investigate ARQ policies for robust packet-based JPEG 2000 image transmission over 3G mobile communication systems, and highlight how the proposed data structure can be exploited to improve the end-to-end performance (1). Index Terms--Wireless JPEG 2000, JPWL, ARQ, error control, robust wireless image transmission, 3G communications.

Published
2003

49. The new CCSDS standard for low-complexity lossless and near-lossless multispectral and hyperspectral image compression

Author

Aaron, Kiely, Matthew, Klimesh, Ian, Blanes, Jonathan, Ligo, Magli, Enrico, Nazeeh, Aranki, Michael, Burl, Roberto, Camarero, Michael, Cheng, Sam, Dolinar, David, Dolman, Greg, Flesch, Hamid, Ghassemi, Martin, Gilbert, Miguel, Hernández-Cabronero, Didier, Keymeulen, Martin, Le, Huy, Luong, Christopher, Mcguinness, Gilles, Moury, Thang, Pham, Martin, Plintovic, Frederic, Sala, Lucana, Santos, Alan, Schaar, Joan, Serra-Sagristà, Simon, Shin, Brenton, Sundlie, Valsesia, Diego, Raffaele, Vitulli, Englin, Wong, William, Wu, Hua, Xie, and Hanying, Zhou

Published
2018

50. Optical compressive sensing technologies for space applications: Instrumental concepts and performance analysis

Author

Guzzi, Donatella, Coluccia, Giulio, Labate, Demetrio, Lastri, Cinzia, Magli, Enrico, Nardino, Vanni, Palombi, Lorenzo, Pippi, Ivan, Coltuc, Daniela, Barducci, Alessandro, Zuccaro Marchi, Alessandro, and Raimondi, Valentina

Subjects
Memory buffer register, Hyperspectral imaging, Computer science, Iterative reconstruction, Compressive sensing, NEO, computer.software_genre, MIR camera, Information extraction, Compressed sensing, Computer engineering, Compression ratio, DMD, Point (geometry), Detection theory, Stellar spectrophotometry, computer
Abstract

This paper presents the results of a study aimed at investigating the potential of Compressive Sensing (CS) technologies for optical space instruments. Besides assessing the pros and cons for a wide set of proposed instrumental concepts for space applications, the study analyzed in further detail two CS-based instrument concepts, each targeting a specific application: an UV-VIS hyperspectral imager on orbiter for stellar spectro-photometry and a MIR camera for sky observation and real-time detection of Near Earth Objects (NEO). The proposed UV-VIS hyperspectral imager relies on a classical CS approach and addresses the CS reconstruction of the full image in order to implement slitless spectrophotometry of stars. The CS-based MIR camera for NEO detection instead explores a novel approach aiming at information extraction without a prior full reconstruction of the image. Besides outlining the optical design of the instruments, its key elements and a pros and cons analysis of the architecture, this paper presents the performance assessment of these instruments for typical application scenarios by means of simulated data. The results showed that, from the point of view of data reconstruction quality, a good performance can be achieved by the designed instruments in terms of compression ratio (CR) and image reconstruction. In terms of system budgets, the CS architecture offered only some marginal benefits with respect to their traditional counterparts, mainly due to the lack of a compression board. Most advantages are instead provided in terms of downlink requirements and memory buffer.

Published
2018
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