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Multi-dimensional Signal Recovery Using Low-rank Deconvolution

DOI:
10.60864/hmpj-sd38
Citation Author(s):
David Reixach
Submitted by:
David Reixach
Last updated:
17 November 2023 - 12:07pm
Document Type:
Poster
Document Year:
2023
Event:
Presenters:
David Reixach
 

In this work we present Low-rank Deconvolution, a powerful framework for low-level feature-map learning for efficient signal representation with application to signal recovery. Its formulation in multi-linear algebra inherits properties from convolutional sparse coding and low-rank approximation methods as in this setting signals are decomposed in a set of filters convolved with a set of low-rank tensors. We show its advantages by learning compressed video representations and solving image in-painting problems.

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