Titlebook: Compressed Sensing and Its Applications; Third International Holger Boche,Giuseppe Caire,Philipp Petersen Book 2019 Springer Nature Switze

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https://doi.org/10.1007/978-3-658-31614-3ey, we give an overview of the state-of-the-art rigorous reconstruction results that have been obtained for three popular quantization models: one-bit quantization, uniform scalar quantization, and noise-shaping methods.

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Strategisches Supply Chain Management-the-art regularization techniques. Moreover, we focus on some advanced aspects arising in practice with THz cameras, which lead to nonlinear reconstruction problems: the calibration of the beam reminiscent of the Retinex problem in imaging and phase recovery problems. Finally, we provide an outlook to future challenges in the area.

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Reconstruction Methods in THz Single-Pixel Imaging,-the-art regularization techniques. Moreover, we focus on some advanced aspects arising in practice with THz cameras, which lead to nonlinear reconstruction problems: the calibration of the beam reminiscent of the Retinex problem in imaging and phase recovery problems. Finally, we provide an outlook to future challenges in the area.

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Book 2019ities.Non-convex optimization.Image reconstruction.Statistical learning theory.This volume will be a valuable resource for graduate students and researchers in the areas of mathematics, computer science, and engineering, as well as other applied scientists exploring potential applications of compressed sensing..

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An Introduction to Compressed Sensing,rnerstone of the underlying theory is the study of inverse problems with linear or nonlinear measurements. Whether it is sparsity, low-rankness, or other familiar notions of low complexity, the theory addresses necessary and sufficient conditions behind the measurement process to guarantee signal re

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,On Reconstructing Functions from Binary Measurements, with functions taking only the values 0 and 1. We consider three particular methods for this problem, the parameterized-background data-weak (PBDW) method, generalized sampling and infinite-dimensional compressed sensing. The first two methods are dependent on knowing the stable sampling rate when

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Classification Scheme for Binary Data with Extensions,ded to be efficient in terms of computation and storage while also allowing for a rigorous mathematical analysis. After providing some motivation, we present our method and analyze its performance for a simple data model. We also discuss extensions of the method to the hierarchical data setting, and

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