access icon free Structure–texture image decomposition using a new non-local TV-Hilbert model

© The Institution of Engineering and Technology
Received 30/03/2019, Accepted 07/04/2020, Revised 30/03/2020, Published 15/04/2020

Combining the advantages of the non-local total variation (TV) and the Gabor function, a new Gabor function based non-local TV-Hilbert model is presented to separate the structure and texture components of the image. Computationally, by introducing the dual form of the non-local TV, the authors reformulate the non-local TV-Hilbert minimisation problem into a convex–concave saddle-point problem. In the aspect of solving algorithm, by transforming the Chambolle–Pock's first-order primal–dual algorithm into a different equivalent form. The authors propose a proximal-based primal–dual algorithm to solve the convex–concave saddle-point problem. At last, experimental results demonstrate that the proposed new model outperforms several existing state-of-the-art variational models.

Inspec keywords: variational techniques; convex programming; minimisation; image denoising; image texture

Other keywords: convex–concave saddle-point problem; nonlocal total variation; Gabor function; proximal-based primal–dual algorithm; nonlocal TV-Hilbert model; structure–texture image decomposition; nonlocal TV-Hilbert minimisation problem; Chambolle–Pock's first-order primal–dual algorithm; texture components

Subjects: Optical, image and video signal processing; Optimisation techniques; Optimisation techniques; Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques

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