The received data of synthetic aperture radar (SAR) will be incomplete or corrupted for many reasons, e.g.,contamination of time series and interference of othe ...

Learning spatially adaptive L1 norms weights for convolutional synthesis-based regularization using algorithm unrolling. We are currently working on integrating all necessary building blocks for the ...

Regularization Term: Integrate the chosen penalty term (alpha multiplied by either L1 or L2 norm) to the model's cost function. Tune Hyperparameters: Adjust alpha through cross-validation for ...

In my experience, L1 regularization, or Lasso, has proven invaluable for improving model fit, especially when dealing with datasets that may contain irrelevant or redundant features.

We introduced a reweighted L1 norm regularization inversion method to complement the impedance boundary amplitude information and thus improve the sparsity of the sparse constraint. Despite the ...

The authors also applied L1-norm regularization to 'AE-linear-linear' model and also displayed how the performance changes by the regularization parameter. However, this means the differences between ...

Petroleum Science (2023). [2] Pre-Stack Seismic Inversion With L1–2-Norm Regularization Via A Proximal DC Algorithm And Adaptive Strategy. Surveys in Geophysics (2022).

Firstly, by taking the phase error into account in the model construction, the proposed method estimates the phase error by solving an L1-norm regularization problem. Then BiIST algorithm is used to ...