The Total Variation Regularized Model for Multiscale Decomposition
Multiscale Modeling & Simulation, 2007•SIAM
This paper studies the total variation regularization with an L^1 fidelity term (TV-L^1) 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-L^1 model is able to separate
image features according to their scales, where the scale is analytically defined by the G-
value. A number of other properties including the geometric and morphological invariance of …
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-L^1 model is able to separate
image features according to their scales, where the scale is analytically defined by the G-
value. A number of other properties including the geometric and morphological invariance of …
This paper studies the total variation regularization with an fidelity term (TV‐) 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‐ model is able to separate image features according to their scales, where the scale is analytically defined by the G‐value. A number of other properties including the geometric and morphological invariance of the TV‐ model are also proved and their applications discussed.