Abstract
In this paper, we propose an improved model for image decomposition and denoising based on Shearlet and nonlocal data fidelity term. The model splits an image into three parts: the cartoon component modeled by total variation (TV) space, the texture component modeled by G space, and the noise component modeled by Shearlet smoothness space. We introduce Shearlet smoothness space to model noise component due to the appreciate property of directional sensitivity. We also incorporate nonlocal weight to the data fidelity term of the new model, in order to reduce the drawbacks of TV regularization presenting in the restored image. The experiment results demonstrate that the new model performs better in image decomposition and denoising than previous models and effectively removes the defaults of TV regularization thanks to the nonlocal data fidelity term.
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