2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDTEC-6.7
Paper Title INTERPRETABLE DEEP IMAGE PRIOR METHOD INSPIRED IN LINEAR MIXTURE MODEL FOR COMPRESSED SPECTRAL IMAGE RECOVERY
Authors Tatiana Gelvez, Jorge Bacca, Henry Arguello, Universidad Industrial de Santander, Colombia
SessionTEC-6: Image and Video Processing 2
LocationArea G
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic Image and Video Processing: Restoration and enhancement
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper presents a recovery method for compressive spectral imaging (CSI) based on the training-data independent deep image prior approach, where the prior information of the image is learned through the weights and the structure of the neural network. Specifically, we propose an interpretable architecture inspired in the linear mixture model for spectral images, where the image is decomposed as the product between a basis matrix, known as endmembers, and a coefficient matrix, known as abundances. These matrices are learned as the weights and the features of the proposed network, respectively. Simulations and experiments show that the proposed recovery method outperforms the state-of-the-art CSI recovery methods, even against training-data dependent methods. Furthermore, the architecture structure inspired by the linear mixture model gives interpretability of some outputs that can be useful for subsequent high-level image processing.