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.

为了解决基于样例的图像修复中不匹配和结构分离的问题，提出了一种基于改进的总变异最小化方法的图像完成算法，称为ETVM。已经使用改进的总方差最小化方法提取了图像的结构，并且现有方法充分利用了图像的已知信息。可以根据图像结构的方向实现鲁棒的填充机制，并且其噪声比原始图像少。重新定义了优先术语，以消除产品影响并确保数据术语始终有效。修复补丁和最佳匹配补丁的优先级取决于修复补丁中已知信息的相似性和未知信息的一致性。与认知计算图像算法的比较表明，该方法可以确保更好地选择要填充的候选图像像素，并且与其他方法相比，可以更好地实现图像完成的全局一致性。噪声图像的修复结果表明，该方法具有很好的鲁棒性，并且可以得到很好的噪声图像修复效果。