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By leveraging prior information about the observed signal, such as sparsity in a known dictionary, GAMP enables reconstructing signals from under-determined measurements – known as compressed sensing.
In the sparse signal setting, most existing signal priors for GAMP assume the input signal to have i.i.d. entries.
We present sparse signal priors to estimate non-identically distributed signals through a non-uniform weighting, e.g. enabling model-based compressed sensing with GAMP.
|Publication date||22 Nov 2018|
|Publication status||Published - 22 Nov 2018|
|Event||international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques - Centre International de Rencontres Mathématiques, Marseille, France|
Duration: 21 Nov 2018 → 23 Nov 2018
Conference number: 4
|Workshop||international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques|
|Location||Centre International de Rencontres Mathématiques|
|Period||21/11/2018 → 23/11/2018|
- compressed sensing
- signal processing
- estimation theory
FingerprintDive into the research topics of 'Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals'. Together they form a unique fingerprint.
- 1 Conference presentations
- 1 Paper without publisher/journal
Oxvig, C. S. & Arildsen, T., 22 Nov 2018. 3 p.
Research output: Contribution to conference without publisher/journal › Paper without publisher/journal › Research › peer-reviewOpen AccessFile