Research on image inpainting algorithm of improved total variation minimization method

In order to solve the issue mismatching and structure disconnecting in exemplar-based image inpainting, an image completion algorithm based on improved total variation minimization method had been proposed in the paper, refer as ETVM. The structure of image had been extracted using improved total variation minimization method, and the known information of image is sufficiently used by existing methods. The robust filling mechanism can be achieved according to the direction of image structure and it has less noise than original image. The priority term had been redefined to eliminate the product effect and ensure data term had always effective. The priority of repairing patch and the best matching patch are determined by the similarity of the known information and the consistency of the unknown information in the repairing patch. The comparisons with cognitive computing image algorithms had been shown that the proposed method can ensure better selection of candidate image pixel to fill with, and it is achieved better global coherence of image completion than others. The inpainting results of noisy images show that the proposed method has good robustness and can also get good inpainting results for noisy images.