Dr. Peter Jung Profile

Dr. Peter Jung

at Deutsches Zentrum für Luft- und Raumfahrt eV

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Publications (2)

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Proceedings Article | 20 November 2024 Presentation + Paper

Compressed learning-based onboard semantic compression for remote sensing platforms

Protim Bhattacharjee, Peter Jung

Proceedings Volume 13196, 1319608 (2024) https://doi.org/10.1117/12.3031472

KEYWORDS: Compressed sensing, Data modeling, Remote sensing, Cameras, Image compression, Signal to noise ratio, Deep learning

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Earth observation (EO) plays a crucial role in creating and sustaining a resilient and prosperous society that has far reaching consequences for all life and the planet itself. Remote sensing platforms like satellites, airborne platforms, and more recently dones and UAVs are used for EO. They collect large amounts of data and this needs to be downlinked to Earth for further processing and analysis. Bottleneck for such high throughput acquisition is the downlink bandwidth. Data-centric solutions to image compression is required to address this deluge. In this work, semantic compression is studied through a compressed learning framework that utilizes only fast and sparse matrix-vector multiplication to encode the data. Camera noise and a communication channel are the considered sources of distortion. The complete semantic communication pipeline then consists of a learned low-complexity compression matrix that acts on the noisy camera output to generate onboard a vector of observations that is downlinked through a communication channel, processed through an unrolled network and then fed to a deep learning model performing the necessary downstream tasks; image classification is studied. Distortions are compensated by unrolling layers of NA-ALISTA with a wavelet sparsity prior. Decoding is thus a plug-n-play approach designed according to the camera/environment information and downstream task. The deep learning model for the downstream task is jointly fine-tuned with the compression matrix and the unrolled network through the loss function in an end-to-end fashion. It is shown that addition of a recovery loss along with the task dependent losses improves the downstream performance in noisy settings at low compression ratios.

Proceedings Article | 24 August 2017 Presentation + Paper

Blind demixing and deconvolution with noisy data at near optimal rate

Dominik Stöger, Peter Jung, Felix Krahmer

Proceedings Volume 10394, 103941E (2017) https://doi.org/10.1117/12.2271571

KEYWORDS: Deconvolution, Compressed sensing, Radar, Radar imaging, Data communications

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Blind demixing and deconvolution refers to the problem of simultaneous deconvolution of several source signals from its noisy superposition. This problem appears, amongst others, in the field of Wireless Communication: Many sensors sporadically communicate only short messages over unknown channels. We show that robust recovery of message and channel vectors can be achieved via convex recovery. This requires that random linear encoding is applied at the devices and that the number of required measurements at the receiver scales essentially with the degrees of freedom of the overall estimation problem. Thus, the scaling is linear in the number of source signals. This significantly improves previous results.

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