Sampling at unknown locations: uniqueness and reconstruction under constraints

Co-authors

Golnoosh Elhami, Michalina Pacholska, Benjamín Béjar Haro and Martin Vetterli.


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Full text: View at publisher, Infoscience.
Cite: Bibtex.
Code: Infoscience.


Abstract

Traditional sampling results assume that the sample locations are known. Motivated by simultaneous localization and mapping (SLAM) and structure from motion (SfM), we investigate sampling at unknown locations. Without further constraints, the problem is often hopeless. For example, we recently showed that, for polynomial

Bound and conquer: Improving triangulation by enforcing consistency

Co-authors

Alireza Ghasemi and Martin Vetterli.


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Preprint: arXiv, Infoscience.
Cite: Bibtex.
Code: Infoscience.


Abstract

We study the accuracy of triangulation in multi-camera systems with respect to the number of cameras. We show that, under certain conditions, the optimal achievable reconstruction error decays quadratically as more cameras are added to the system. Furthermore, we analyse the error decay-rate of major state-of-the-art algorithms with respect to the number

Sampling and Exact Reconstruction of Pulses with Variable Width

Co-authors

Gilles Baechler, Loïc Baboulaz and Martin Vetterli.


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Cite: Bibtex.
Code: Infoscience.


Abstract

Recent sampling results enable the reconstruction of signals composed of streams of fixed-shaped pulses. These results have found applications in topics as varied as channel estimation, biomedical imaging and radio astronomy. However, in many real signals, the pulse shapes vary throughout the signal. In this paper, we

Quadtree Structured Image Approximation for Denoising and Interpolation

Co-author

Pier Luigi Dragotti.


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Full text: View at publisher, Infoscience.
Cite: Bibtex.
Code: Infoscience.


Abstract

The success of many image restoration algorithms is often due to their ability to sparsely describe the original signal. Shukla proposed a compression algorithm, based on a sparse quadtree decomposition model, which could optimally represent piecewise polynomial images. In this paper, we adapt this model to the image restoration by changing

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