Zobrazeno 1 - 10
of 133
pro vyhledávání: '"Oberlin, Thomas"'
Autor:
Kouakou, Hugues, Goulart, José Henrique de Morais, Vitale, Raffaele, Oberlin, Thomas, Rousseau, David, Ruckebusch, Cyril, Dobigeon, Nicolas
This work introduces an on-the-fly (i.e., online) linear unmixing method which is able to sequentially analyze spectral data acquired on a spectrum-by-spectrum basis. After deriving a sequential counterpart of the conventional linear mixing model, th
Externí odkaz:
http://arxiv.org/abs/2407.15636
In recent years, driven by the need for safer and more autonomous transport systems, the automotive industry has shifted toward integrating a growing number of Advanced Driver Assistance Systems (ADAS). Among the array of sensors employed for object
Externí odkaz:
http://arxiv.org/abs/2402.08427
Regularization of inverse problems is of paramount importance in computational imaging. The ability of neural networks to learn efficient image representations has been recently exploited to design powerful data-driven regularizers. While state-of-th
Externí odkaz:
http://arxiv.org/abs/2311.17744
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems, 2024
Automotive radar sensors provide valuable information for advanced driving assistance systems (ADAS). Radars can reliably estimate the distance to an object and the relative velocity, regardless of weather and light conditions. However, radar sensors
Externí odkaz:
http://arxiv.org/abs/2212.11172
Audio inpainting, i.e., the task of restoring missing or occluded audio signal samples, usually relies on sparse representations or autoregressive modeling. In this paper, we propose to structure the spectrogram with nonnegative matrix factorization
Externí odkaz:
http://arxiv.org/abs/2206.13768
This paper considers the phase retrieval (PR) problem, which aims to reconstruct a signal from phaseless measurements such as magnitude or power spectrograms. PR is generally handled as a minimization problem involving a quadratic loss. Recent works
Externí odkaz:
http://arxiv.org/abs/2204.01360
Autor:
Oberlin, Thomas, Verm, Mathieu
This paper proposes a new way of regularizing an inverse problem in imaging (e.g., deblurring or inpainting) by means of a deep generative neural network. Compared to end-to-end models, such approaches seem particularly interesting since the same net
Externí odkaz:
http://arxiv.org/abs/2101.08661
Phase retrieval aims to recover a signal from magnitude or power spectra measurements. It is often addressed by considering a minimization problem involving a quadratic cost function. We propose a different formulation based on Bregman divergences, w
Externí odkaz:
http://arxiv.org/abs/2011.12818
Autor:
Cavalcanti, Yanna Cruz, Oberlin, Thomas, Ferraris, Vinicius, Dobigeon, Nicolas, Ribeiro, Maria, Tauber, Clovis
When no arterial input function is available, quantification of dynamic PET images requires a previous step devoted to the extraction of a reference time-activity curve (TAC). Factor analysis is often applied for this purpose. This paper introduces a
Externí odkaz:
http://arxiv.org/abs/2011.10097
Time-frequency audio source separation is usually achieved by estimating the short-time Fourier transform (STFT) magnitude of each source, and then applying a phase recovery algorithm to retrieve time-domain signals. In particular, the multiple input
Externí odkaz:
http://arxiv.org/abs/2010.10255