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Compact Representation of n-th order TGV
Although regularization methods based on derivatives are favored for their robustness and computational simplicity, research exploring higher-order derivatives remains limited.
Structurally Adaptive Multi-Derivative Regularization for Image Recovery from Sparse Fourier Samples
In this paper, we develop a regularization method that outperforms compressive sensing methods as well as selected learning-based methods, without any need for training data.
Generalized Hessian-Schatten Norm Regularization for Image Reconstruction
We name the new regularization as the generalized Hessian-Schatten norm regularization (GHSN), and we develop a novel optimization method for image reconstruction using the new form of regularization based on the well-known framework called alternating direction method of multipliers (ADMM).
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