Subdifferential Decomposition of 1D-regularized Total Variation with Nonhomogeneous Coefficients

The Total Variation Regularized L1 Model for Multiscale Decomposition

This paper studies the total variation regularization with an L1 fidelity term (TV-L1) model for decomposing an image into features of different scales. We first show that the images produced by this model can be formed from the minimizers of a sequence of decoupled geometry subproblems. Using this result we show that the TV-L1 model is able to separate image features according to their scales,...

Geometry of total variation regularized Lp-model

Rice University Caam Technical Report Tr06-16 the Total Variation Regularized L 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 from the minimizers of a sequence of decoupled geometry subproblems. Using this result we show that the TV-L1 model is able to separate image features according to their scales,...

Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition

Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, and many others. Such complex noise could degrade the quality of the acquired HSIs, limiting the precision of the subsequent processing. In this paper, we present a novel tensor-based HSI restoration approach by fully id...

Image Cartoon-Texture Decomposition and Feature Selection Using the Total Variation Regularized L1 Functional

This paper studies the model of minimizing total variation with an L-norm fidelity term for decomposing a real image into the sum of cartoon and texture. This model is also analyzed and shown to be able to select features of an image according to their scales.