Image Inpainting Algorithm Based on Edge Feature and Pixel Structure Similarity

The patch matching criterion of the Criminisi algorithm could not choose the best sample patch reasonably because single color factor was adopted only, and the single inpainting template could result in the filling cracks and the erroneous pixel during the inpainting process. A new algorithm was proposed to solve these problems. Firstly, a piecewise inpainting combining local features and edge texture resolutions was proposed to enhance the edge texture resolution. Secondly, the sample similarity and the information entropy similarity were used to determine the best sample patch set, and the patch matching criteria was established according to the texture similarity and the Euclidean geometry distance of the color and the feature items. Then, the filling cracks and the erroneous pixel problem of the Criminisi algorithm were solved by the adaptive inpainting template algorithm based on the information entropy. Finally, the fruit fly optimization algorithm was introduced to reduce the time of inpainting image. The experimental results showed that this new algorithm could achieve the satisfactory inpainting effect and the inpainting efficiency for different images.