Abstract
Mathematical methods of image inpainting often involve the discretization of a given continuous model. Typically, this is done by a pointwise discretization. We present a method that avoids this by modeling known variational approaches using a finite dimensional spline space. The way we build our algorithm we are able to prove that the basis of the spline space is stable. Further, due to the compact supports and structure of the basis the model involves a sparse system matrix allowing a fast and memory efficient implementation. Besides the analysis of the resulting model, we present a numerical implementation based on the alternating method of multipliers. We compare the results numerically with classical TV inpainting and give examples of applications such as text removal, restoration and noise removal.
Highlights
In this paper we study the continuous inpainting problem for digital images
In this paper we rather use a finite dimensional function space, the space of tensor product spline functions based on B-splines, see e.g., [3, 4]
We present an algorithm and its implementation using a first-order primal-dual algorithm [24], compare the results of our method to standard total variation (TV) inpainting and show some examples of applications
Summary
Mathematical methods of image inpainting often involve the discretization of a given continuous model. This is done by a pointwise discretization. We present a method that avoids this by modeling known variational approaches using a finite dimensional spline space. The way we build our algorithm we are able to prove that the basis of the spline space is stable. Due to the compact supports and structure of the basis the model involves a sparse system matrix allowing a fast and memory efficient implementation. Besides the analysis of the resulting model, we present a numerical implementation based on the alternating method of multipliers. We compare the results numerically with classical TV inpainting and give examples of applications, such as text removal, restoration, and noise removal
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