Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Jan Christian Hauffen"'
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 9 (2023)
In this study, we consider algorithm unfolding for the multiple measurement vector (MMV) problem in the case where only few training samples are available. Algorithm unfolding has been shown to empirically speed-up in a data-driven way the convergenc
Externí odkaz:
https://doaj.org/article/7d8fa745c5aa4ac1a499ae716f500048
Autor:
Jan Christian Hauffen, Linh Kästner, Samim Ahmadi, Peter Jung, Giuseppe Caire, Mathias Ziegler
Publikováno v:
Sensors, Vol 22, Iss 15, p 5533 (2022)
Block-sparse regularization is already well known in active thermal imaging and is used for multiple-measurement-based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment.
Externí odkaz:
https://doaj.org/article/ae6aa7ff32df40af9b7f40c7ddab0cb8
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 71:1-9
This paper presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super resolution (SR) imaging. Photothermal imaging is a well-known technique in active thermography for nondestructive inspection of de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6edcbd6021cf07e34805197862ea8bbc
https://doi.org/10.1109/tim.2022.3154803
https://doi.org/10.1109/tim.2022.3154803
Autor:
Jan Christian Hauffen, Linh Kästner, Samim Ahmadi, Peter Jung, Giuseppe Caire, Mathias Ziegler
Publikováno v:
Sensors; Volume 22; Issue 15; Pages: 5533
Block-sparse regularization is already well-known in active thermal imaging and is used for multiple measurement based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d9475f28a8439d62dce6635f84c204c7
