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Found 5 papers, 2 papers with code
Ambiguity in solving imaging inverse problems with deep learning based operators
Numerical experiments are performed to verify the accuracy and stability of the proposed approaches for image deblurring when unknown or not-quantified noise is present; the results confirm that they improve the network stability with respect to noise.
To be or not to be stable, that is the question: understanding neural networks for inverse problems
The solution of linear inverse problems arising, for example, in signal and image processing is a challenging problem since the ill-conditioning amplifies, in the solution, the noise present in the data.
Minimizing L1 over L2 norms on the gradient
Consequently, we postulate that applying L1/L2 on the gradient is better than the classic total variation (the L1 norm on the gradient) to enforce the sparsity of the image gradient.
Limited-angle CT reconstruction via the L1/L2 minimization
In this paper, we consider minimizing the L1/L2 term on the gradient for a limited-angle scanning problem in computed tomography (CT) reconstruction.
LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled Variables
LAP is most promising for cases when the subproblem corresponding to one of the variables is considerably easier to solve than the other.
