Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery

• URL: http://arxiv.org/abs/2211.02973v1
• Date: Sat, 5 Nov 2022 21:32:25 GMT
• Title: Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery
• Authors: Tatiana Gelvez-Barrera, Jorge Bacca, Henry Arguello
• Abstract summary: This paper proposes a non-data-driven deep neural network for spectral image recovery problems. The proposed approach, dubbed Mixture-Net, implicitly learns the prior information through the network. Experiments show the MixtureNet outperforming state-of-the-art methods in recovery quality with the advantage of architecture interpretability.
• Score: 22.0246327137227
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper proposes a non-data-driven deep neural network for spectral image recovery problems such as denoising, single hyperspectral image super-resolution, and compressive spectral imaging reconstruction. Unlike previous methods, the proposed approach, dubbed Mixture-Net, implicitly learns the prior information through the network. Mixture-Net consists of a deep generative model whose layers are inspired by the linear and non-linear low-rank mixture models, where the recovered image is composed of a weighted sum between the linear and non-linear decomposition. Mixture-Net also provides a low-rank decomposition interpreted as the spectral image abundances and endmembers, helpful in achieving remote sensing tasks without running additional routines. The experiments show the MixtureNet effectiveness outperforming state-of-the-art methods in recovery quality with the advantage of architecture interpretability.

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