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Deep coupled-representation learning for sparse linear inverse problems with side information This publication appears in: IEEE Signal Processing Letters Authors: E. Tsiligianni and N. Deligiannis Volume: 26 Issue: 12 Pages: 1768-1772 Publication Date: Dec. 2019
Abstract: In linear inverse problems, the goal is to recover a target signal from undersampled, incomplete or noisy linear measurements. Typically, the recovery relies on complex numerical optimization methods recent approaches perform an unfolding of a numerical algorithm into a neural network form, resulting in a substantial reduction of the computational complexity. In this letter, we consider the recovery of a target signal with the aid of a correlated signal, the so-called side information (SI), and propose a deep unfolding model that incorporates SI. The proposed model is used to learn coupled representations of correlated signals from different modalities, enabling the recovery of multi-modal data at a low computational cost. As such, our work introduces the first deep unfolding method with SI, which actually comes from a different modality. We apply our model to reconstruct near-infrared images from undersampled measurements given RGB images as SI. Experimental results demonstrate the superior performance of the proposed framework against single-modal deep learning methods that do not use SI, multi-modal deep learning designs, and optimization algorithms.
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