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Off-The-Grid Model Based Deep Learning (O-Modl) | IEEE Conference Publication | IEEE Xplore

Off-The-Grid Model Based Deep Learning (O-Modl)


Abstract:

We introduce a model based off-the grid image reconstruction algorithm using deep learned priors. The main difference of the proposed scheme with current deep learning st...Show More

Abstract:

We introduce a model based off-the grid image reconstruction algorithm using deep learned priors. The main difference of the proposed scheme with current deep learning strategies is the learning of non-linear annihilation relations in Fourier space. We rely on a model based framework, which allows us to use a significantly smaller deep network, compared to direct approaches that also learn how to invert the forward model. Preliminary comparisons against image domain MoDL approach demonstrates the potential of the off-the-grid formulation. The main benefit of the proposed scheme compared to structured low-rank methods is the quite significant reduction in computational complexity.
Date of Conference: 08-11 April 2019
Date Added to IEEE Xplore: 11 July 2019
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Conference Location: Venice, Italy

References

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