Exemplar-based image inpainting algorithm using adaptive sample and candidate patch system

In the existing exemplar based image inpainting algorithms, the most similar match patches are used to inpaint the destroyed region, and they are searched in the whole source region in a fixed size. However, sometimes it would decrease the connectivity of structure and clearness of texture while increases the time complexity of this algorithm. To solve these problems, firstly it proposed an adaptive sample algorithm based on patch sparsity, it calculates the patch sparsity and through it dividing the patches location into three types. And then the size of the sample patch would be adaptively changed according to the type. Secondly it proposed a candidate patch system to improve the patch matching rate. From the result, we can see that the proposed method can match more significant patches than the traditional method, and it can give a better texture inpainting effect, especially when processing the images with complex and regular textures, and the image with large destroyed region.