Abstract
In view of the drawback of most image inpainting algorithms by which texture was not prominent, an adaptive inpainting algorithm based on continued fractions was proposed in this paper. In order to restore every damaged point, the information of known pixel points around the damaged point was used to interpolate the intensity of the damaged point. The proposed method included two steps; firstly, Thiele’s rational interpolation combined with the mask image was used to interpolate adaptively the intensities of damaged points to get an initial repaired image, and then Newton-Thiele’s rational interpolation was used to refine the initial repaired image to get a final result. In order to show the superiority of the proposed algorithm, plenty of experiments were tested on damaged images. Subjective evaluation and objective evaluation were used to evaluate the quality of repaired images, and the objective evaluation was comparison of Peak Signal to Noise Ratios (PSNRs). The experimental results showed that the proposed algorithm had better visual effect and higher Peak Signal to Noise Ratio compared with the state-of-the-art methods.
Highlights
Image inpainting is an important branch of image processing which studies how to restore the damaged part based on human visual mechanism
In the adaptive inpainting phase, we adopt Thiele’s rational interpolation function and the corresponding mask image to interpolate the intensities of damaged pixel points
We scan the mask image line by line and find the positions of damaged pixel points, and, for every damaged pixel point, we adopt the information of known pixel points near it and Thiele’s rational interpolation function to interpolate its intensity
Summary
Image inpainting is an important branch of image processing which studies how to restore the damaged part based on human visual mechanism. Pascal et al [4] proposed a new compression framework with homogeneous, biharmonic, and edge-enhancing diffusion which supported different strategies for data selection and storage and gave a detailed analysis of the advantages and disadvantages of the three partial differential equations (PDEs) These methods based on PDE are suitable for straight lines, curves, and small regions, and they are not suitable for texture details of large areas. In [15], an image inpainting algorithm based on Kriging interpolation technique was proposed, where the Kriging interpolation technique automatically could fill the damaged region and scratched regions These methods above have good repaired effect, by which texture details cannot be well processed. The main contributions of this paper are as follows: the adaptive inpainting scheme based on Thiele’s rational interpolation is proposed; the novel inpainting model by Thiele’s rational interpolation combined with Newton-Thiele’s rational interpolation is proposed
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