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as soon as is submitted to ZB.
Noisy rin Unlimited svia adaptive modulo representations.
In: (2024 International Workshop on the Theory of Computational Sensing and its Applications to Radar, Multimodal Sensing and Imaging, CoSeRa 2024). 2024. 47-51 (2024 International Workshop on the Theory of Computational Sensing and its Applications to Radar, Multimodal Sensing and Imaging, CoSeRa 2024)
Recent works put forth the Unlimited Sensing Framework (USF), a novel approach to analog-to-digital conversion for high dynamic range sensing. It addresses the saturation phenomenon that commonly arises when physical measurements exceed the dynamic range of a sensor, yielding permanent loss of the input data. However, the USF still has some limitations when dealing with random noise. In the present paper, we propose a novel iterative method to tackle unlimited sensing in a noisy setting. In one step, our approach applies local transformations of the range to remove strong artifacts caused by the noise on local subdivisions of the domain. In the following step, the signal is then approximated via a least squares method. These two types of steps are then alternated. We illustrate the performances of our algorithm in high noise regime.
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Publication type
Article: Conference contribution
Keywords
Analog-to-digital ; High-dynamic Range ; Inverse Problems ; Least-squares ; Modulo Sampling ; Shannon Sampling
ISSN (print) / ISBN
[9798350365504]
Conference Title
2024 International Workshop on the Theory of Computational Sensing and its Applications to Radar, Multimodal Sensing and Imaging, CoSeRa 2024
Quellenangaben
Pages: 47-51
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Institute(s)
Institute of Biological and Medical Imaging (IBMI)