Now showing items 1-4 of 4
The Total Variation Regularized L1 Model for Multiscale Decomposition
This paper studies the total variation regularization model with an L1 fidelity term (TV-L1) for decomposing an image into features of different scales. We first show that the images produced by this model can be formed ...
Learning Circulant Sensing Kernels
In signal acquisition, Toeplitz and circulant matrices are widely used as sensing operators. They correspond to discrete convolutions and are easily or even naturally realized in various applications. For compressive ...
Error Forgetting of Bregman Iteration
This short article analyzes an interesting property of the Bregman iterative procedure for minimizing a convex piece-wise linear function J(x) subject to linear constraints Ax=b. The procedure obtains its solution by solving ...
A Comparison of Three Total Variation Based Texture Extraction Models
This paper qualitatively compares three recently proposed models for signal/image texture extraction based on total variation minimization:the Meyer, Vese-Osher, and TV-L1 models. We formulate discrete versions of these ...