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

Shape from bandwidth: The 2-D orthogonal projection case

Co-authors

Benjamín Bejar Haro and Martin Vetterli.


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Full text: View at publisher, Infoscience.
Cite: Bibtex.
Code: Run in browser (using binder), Infoscience.


Abstract

Could bandwidth – one of the most classic concepts in signal processing – have a new purpose? In this paper, we investigate the feasibility of using bandwidth to infer shape from a single image. As a first analysis, we limit our

Unlabeled sensing: Reconstruction algorithm and theoretical guarantees

Co-authors

Golnoosh Elhami, Benjamín Bejar Haro and Martin Vetterli.


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


Abstract

It often happens that we are interested in reconstructing an unknown signal from partial measurements. Also, it is typically assumed that the location (temporal or spatial) of each sample is known and that the only distortion present in the observations is due to additive measurement noise. However, there are some applications

Sampling and Exact Reconstruction of Pulses with Variable Width

Co-authors

Gilles Baechler, Loïc Baboulaz and Martin Vetterli.


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Full text: View at publisher, Infoscience.
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

Accurate image registration using approximate Strang-Fix and an application in super-resolution

Co-author

Pier Luigi Dragotti.


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


Abstract

Accurate registration is critical to most multi-channel signal processing setups, including image super-resolution. In this paper we use modern sampling theory to propose a new robust registration algorithm that works with arbitrary sampling kernels. The algorithm accurately approximates continuous-time Fourier coefficients from discrete-time samples. These Fourier coefficients can be used to

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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