Abstract: The traditional texture synthesis image inpainting approaches can only extract useful information from the damaged image, but cannot deal with the complex structures. In the meanwhile, the deep-learning-based ones usually have long training time and unsatisfactory texture synthesis effects. To solve the problems, this paper proposes an image inpainting approach based on similar image registration. First, a similarity calculation method of damaged image is proposed by using the deep learning features of images, thus the most similar image of the damaged ones in dataset can be found to provide more useful information for the image inpainting process. Second, this paper matches the damaged image with its similar ones and use the homography transform to realize the automatic rough correction of image space position. At last, the texture synthesis effects are improved by using the improved optimal patch searching method and the relative matching criteria, then the image inpainting is performed. Simulation results demonstrate that the approach can obtain more useful information, yield perfect texture synthesis effect, and overcome the shortcomings of the traditional deep-learning-based and texture synthesis approaches. Besides that, the proposed approach can also obtain ideal inpainting effects even for the damaged images with complex textural information and structures.