Embedded polarizing filters to separate diffuse and specular reflection

Co-authors

Laurent Jospin and Gilles Baechler.


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Full text: Arxiv.
Cite: Bibtex.
Code: Will appear shortly.


Abstract

Polarizing filters provide a powerful way to separate diffuse and specular reflection; however, traditional methods rely on several captures and require proper alignment of the filters. Recently, camera manufacturers have proposed to embed polarizing micro-filters in front of the sensor, creating a mosaic of pixels with different polarizations. In this paper, we investigate the

Combining Range and Direction for Improved Localization

Co-authors

Gilles Baechler, Frederike Dümbgen, Golnoosh Elhami, Miranda Krekovic, Robin Scheibler and Martin Vetterli.


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Full text: Infoscience.
Cite: Bibtex.
Code: GitHub.


Abstract

Self-localization of nodes in a sensor network is typically achieved using either range or direction measurements; in this paper, we show that a constructive combination of both improves the estimation. We propose two localization algorithms that make use of the differences between the sensors’ coordinates, or

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

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

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