SGTD: Structure Gradient and Texture Decorrelating Regularization for Image Decomposition
2013
This paper presents a novel structure gradient and texture
decorrelating regularization (SGTD) for image decomposition.
The motivation of the idea is under the assumption
that the structure gradient and texture components should
be properly decorrelated for a successful decomposition.
The proposed model consists of the data fidelity term, total
variation regularization and the SGTD regularization. An
augmented Lagrangian method is proposed to address this
optimization issue, by first transforming the unconstrained
problem to an equivalent constrained problem and then applying
an alternating direction method to iteratively solve
the subproblems. Experimental results demonstrate that
the proposed method presents better or comparable performance
as state-of-the-art methods do.
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