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GAN-based Projector for Faster Recovery with Convergence Guarantees in Linear Inverse Problems
A Generative Adversarial Network (GAN) with generator $G$ trained to model the prior of images has been shown to perform better than sparsity-based regularizers in ill-posed inverse problems.
Improving Robustness of Deep-Learning-Based Image Reconstruction
We find that a linear network using the proposed min-max learning scheme indeed converges to the same solution.
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