Using the L-curve for determining optimal regularization parameters
Numerische Mathematik
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration
IEEE Transactions on Image Processing
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This study reviews the theory, programming designs and merits of two new Matlab-based routines for the forward and inverse Radon transform. These routines offer users flexible choices of integration path functions to take advantage of improved Radon-domain identification and isolation of seismic phases. Least-squares inversion of frequency components and judicious choices of regularization techniques enables additional noise suppression and signal enhancement in the Radon domain. The forward Radon transform routine has the added benefit of spatial interpolation for irregularly sampled data. The accuracy and applicability of these two new routines are demonstrated using data sets containing long-period SS precursors and high-frequency receiver functions. With minimal modifications these two highly portable, carefully documented Radon-transform routines could be easily adapted for a broad range of applications.