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An implementation of Projected Gradient Descent DR-CG-Net (PGD DR-CG-Net) and Iterative Shrinkage and Thresholding Algorithm DR-CG-Net (ISTA DR-CG-Net) from
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Lyons C., Raj R. G., & Cheney M. (2023). "Deep Regularized Compound Gaussian Network for Solving Linear Inverse Problems." arXiv preprint arXiv:2311.17248.
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Lyons C., Raj R. G., & Cheney M. (2024). "Deep Regularized Compound Gaussian Network for Solving Linear Inverse Problems," in IEEE Transactions on Computational Imaging, vol. 10, pp. 399-414, 2024, doi: 10.1109/TCI.2024.3369394.
See 'requirements.txt' for the python version, packages, and package versions utilized for this project.
The DR-CG-Net method is an algorithm-unrolled deep neural network (DNN) that solves linear inverse problems to the forward measurement model
Often an iterative algorithm is used to solve inverse problems where statistical prior information on
- Projected Gradient Descent (PGD)
$$g(z,u) = P_Z(z - \eta \nabla_z F(z,u)) = P_Z(z - \eta(A_u^T(A_uz-y)+\nabla R(z)))$$ - Iterative Shrinkage and Thresholding (ISTA)
$$g(z,u) = \text{prox}_{\eta R}(z -\eta A_u^T(A_uz-y)) \coloneqq \arg\min_t \frac{1}{2}||t - (z-\eta A_u^T(A_uz-y))||_2^2+\eta R(t)$$
The diagram below displays the DR-CG-Net architecture, which consists of alternating blocks
DR-CG-Net has been empirically shown to provide state-of-the-art performance when limited training data is available. Below is a sample reconstructed image.