Multiplicative noise removal and blind inpainting of ultrasound images based on a new variational framework

In this paper, we present a novel method for blind image inpainting, which can restore images with missing or corrupted pixels, or images where the location of the damaged pixels is unknown. The method applies weighted nonlocal Laplacian to address the problem of blind image inpainting using low-dimensional manifold model (LDMM) regularization, and uses semi-local blocks instead of point integrals to implement constraints in LDMM. This solves the problem of low solution efficiency caused by the asymmetry of the linear equations solved by point integration, and the problem of the high iteration count to get good restoration effect. Experiments show that our method is competitive with latest methods in terms of both repairing images with large missing pixels rate and inpainting speed.

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Multiplicative noise removal and blind inpainting of ultrasound images based on a new variational framework

Cited by 3 publications

References 38 publications

ECDM: Enhanced edge based coupled deformable model for image segmentation in the presence of speckle noise and severe intensity inhomogeneity

ECDM: Enhanced edge based coupled deformable model for image segmentation in the presence of speckle noise and severe intensity inhomogeneity

CDM: A coupled deformable model for image segmentation with speckle noise and severe intensity inhomogeneity

Blind Image Inpainting Using Low-Dimensional Manifold Regularization

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