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A Sparse Model-Inspired Deep Thresholding Network for Exponential Signal Reconstruction-Application in Fast
IEEE Transactions on Neural Networks and Learning Systems
|February 4, 2022
Summary
This study introduces MoDern, a deep learning (DL) model for fast spectra reconstruction from nonuniformly sampled data. MoDern combines sparse modeling with DL for robust, high-fidelity signal reconstruction, outperforming existing methods.
Area of Science:
- Signal Processing
- Machine Learning
- Biophysics
Background:
- Nonuniform sampling (NUS) accelerates data acquisition but necessitates advanced reconstruction techniques.
- Deep learning (DL) shows promise for signal reconstruction but often lacks robustness and explainability.
- Faithful reconstruction of partially sampled exponential signals is crucial for diverse applications.
Purpose of the Study:
- To develop a robust and explainable deep learning architecture for spectra reconstruction from undersampled data.
- To integrate sparse model-based optimization with data-driven deep learning for improved reconstruction fidelity.
- To create an accessible platform for applying advanced reconstruction methods in biological applications.
Main Methods:
- A novel deep learning architecture, MoDern, was developed by incorporating iterative reconstruction principles within a sparse model framework.
- A learnable soft-thresholding module was designed to adaptively mitigate spectral artifacts caused by undersampling.
- The model was trained using synthetic data and evaluated on both synthetic and biological datasets.
Main Results:
- MoDern demonstrated superior robustness, high-fidelity, and ultrafast reconstruction compared to state-of-the-art methods.
- The model achieved excellent generalization from synthetic training data to diverse biological data scenarios.
- MoDern features a compact network architecture with a minimal number of parameters.
Conclusions:
- MoDern offers a powerful and efficient strategy for spectra reconstruction from undersampled data, addressing limitations of current DL approaches.
- The developed model provides a promising foundation for advancing biological applications requiring fast and accurate signal processing.
- An open-access cloud platform, XCloud-MoDern, was created to facilitate the adoption and further development of these reconstruction techniques.
