We consider compressive sensing problems with model mismatch where one wishes to recover a sparse high-dimensional vector from low-dimensional observations subject to uncertainty in the measurement operator. In particular, we design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. Our proposed network -named Robust lErned Shrinkage-Thresholding (REST) -exhibits additional features including enlarged number of parameters and normalization processing compared to state-of-the-art deep architecture Learned Iterative Shrinkage-Thresholding Algorithm (LISTA), leading to the reliable recovery of the signal under sample-wise varying model mismatch. Our proposed network is also shown to outperform LISTA in compressive sensing problems under sample-wise varying model mismatch.
|Title of host publication||ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)|
|State||Published - 13 May 2021|
|Event||2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Toronto, ON, Canada|
Duration: 6 Jun 2021 → 11 Jun 2021
|Conference||2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)|
|Period||6/06/21 → 11/06/21|