no code implementations • 4 May 2024 • Protim Bhattacharjee, Peter Jung
The black box nature of deep learning models complicate their usage in critical applications such as remote sensing.
no code implementations • 28 Sep 2023 • Kun Qian, Yuanyuan Wang, Peter Jung, Yilei Shi, Xiao Xiang Zhu
Deep neural networks based on unrolled iterative algorithms have achieved remarkable success in sparse reconstruction applications, such as synthetic aperture radar (SAR) tomographic inversion (TomoSAR).
no code implementations • 26 Sep 2023 • Saeid K. Dehkordi, Lorenzo Pucci, Peter Jung, Andrea Giorgetti, Enrico Paolini, Giuseppe Caire
The proposed parameter estimation in this work consists of a two-stage estimation process, where the first stage is based on far-field assumptions, and is used to obtain a first estimate of the target parameters.
no code implementations • 16 Aug 2023 • Fernando Pedraza, Saeid K. Dehkordi, Jan C. Hauffen, Shuangyang Li, Peter Jung, Giuseppe Caire
We investigate radar parameter estimation and beam tracking with a hybrid digital-analog (HDA) architecture in a multi-block measurement framework using an extended target model.
no code implementations • 23 May 2023 • Kun Qian, Yuanyuan Wang, Peter Jung, Yilei Shi, Xiao Xiang Zhu
An emerging technique known as deep unrolling provided a good combination of the descriptive ability of neural networks, explainable, and computational efficiency for BPDN.
no code implementations • 7 Apr 2023 • Hamid El Bahja, Jan Christian Hauffen, Peter Jung, Bubacarr Bah, Issa Karambal
Deep learning has been highly successful in some applications.
1 code implementation • 7 Feb 2022 • Jonathan Sauder, Martin Genzel, Peter Jung
Countless signal processing applications include the reconstruction of signals from few indirect linear measurements.
no code implementations • NeurIPS Workshop Deep_Invers 2021 • Jonathan Sauder, Martin Genzel, Peter Jung
Countless signal processing applications include the reconstruction of an unknown signal from very few indirect linear measurements.
no code implementations • 2 Sep 2021 • Muhammad Fadli Damara, Gregor Kornhardt, Peter Jung
Our experiments on the MNIST and CelebA datasets show that the combination of measurement conditional model with NPGD works well in recovering the compressed signal while achieving similar or in some cases even better performance along with a much faster reconstruction.
no code implementations • 7 Jul 2021 • Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, JongSeok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang, Richard Bamler, Xiao Xiang Zhu
Different examples from the wide spectrum of challenges in different fields give an idea of the needs and challenges regarding uncertainties in practical applications.
no code implementations • 7 Jun 2021 • Udaya S. K. P. Miriya Thanthrige, Peter Jung, Aydin Sezgin
In many scenarios, the number of defects that we are interested in is limited and the signaling response of the layered structure can be modeled as a low-rank structure.
no code implementations • 21 Apr 2021 • Samim Ahmadi, Linh Kästner, Jan Christian Hauffen, Peter Jung, Mathias Ziegler
Photothermal imaging is a well-known technique in active thermography for nondestructive inspection of defects in materials such as metals or composites.
no code implementations • 27 Jan 2021 • Robert Beinert, Peter Jung, Gabriele Steidl, Tom Szollmann
In this work we consider the problem of identification and reconstruction of doubly-dispersive channel operators which are given by finite linear combinations of time-frequency shifts.
Super-Resolution Information Theory Numerical Analysis Information Theory Numerical Analysis 47A62, 65R30, 65T99, 94A20
2 code implementations • 7 Dec 2020 • Samim Ahmadi, Jan Christian Hauffen, Linh Kästner, Peter Jung, Giuseppe Caire, Mathias Ziegler
More precisely, we show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters.
no code implementations • 18 Nov 2020 • Osman Musa, Peter Jung, Giuseppe Caire
source prior.
no code implementations • 31 Oct 2020 • Udaya S. K. P. Miriya Thanthrige, Ali Kariminezhad, Peter Jung, Aydin Sezgin
Here, defects are inside a layered material structure, therefore, due to reflections from the surface of the layered material structure the defect detection is challenging.
no code implementations • 24 Oct 2020 • Linh Kästner, Samim Ahmadi, Florian Jonietz, Mathias Ziegler, Peter Jung, Giuseppe Caire, Jens Lambrecht
Spot welding is a crucial process step in various industries.
no code implementations • 23 Oct 2020 • Freya Behrens, Jonathan Sauder, Peter Jung
It is well-established that many iterative sparse reconstruction algorithms such as ISTA can be unrolled to yield a learnable neural network for improved empirical performance.
1 code implementation • ICLR 2021 • Freya Behrens, Jonathan Sauder, Peter Jung
A prime example is learned ISTA (LISTA) where weights, step sizes and thresholds are learned from training data.
no code implementations • 16 Jul 2020 • Martin Reiche, Peter Jung
This paper shows how data-driven deep generative models can be utilized to solve challenging phase retrieval problems, in which one wants to reconstruct a signal from only few intensity measurements.