Repair Technology Based On Digital Image Of The Total Variation Method

Digital image inpainting is a process of restoration in the lost or damaged domain in digital image. In the digital image processing ,to inpaint is to recover the original painting or image ,where the image has been damaged ,to achieve better vision effect so that we even cannot distinguish the original image from the inpainted one .At present, the digital inpainting has played an important role in the image compressing ,image coding, archaeology in the cultural relic and martial secret.In this paper, We explain the development of the current digital image inpainting techniques such as the diffusion technique ,the technique of matching and the technique of the decomposition .then ,We analyse the theorem of some representative algorithms such as the BSCB model and the decomposition inpainting method created by Criminisi.We mainly study the total variation method and the texture matching method in this paper as the representatives of the diffusion and matching technology respectively. At the same time ,we make some improvements on the basis of the original algorithm and even add the concept of relativity coefficient into the total variation method. In that case, We can inpaint the image through diffusion while considering the effect of the reference point to the object point. In the texture matching method, the size and the searching area of the template block are improved .The experiments show that we get good inpainting results. The inpainted image created by the improved total variation algorithm seems natural in the edge and the vision effect of the inpainted image is greatly improved. The inpainted image created by the texture matching method that make use of the set of the size of the texture template and the searching range of the matching block ,fuse better with the surrounding image. Furthermore, the cost of the time is obvious shortened.In addition, we generalize the applicability of the two algorithms . The experiment results show that when little image information is lost and the edge information is not lost in large extent ,we can apply the improved total variation algorithm and make use of the edge diffusion technology to smooth out the image in order to get better vision effect. Otherwise, when the image or the edge information is lost to a large extent , the texture matching algorithm can fill in the lost information by utilizing the similar texture information in the residual image . The texture matching algorithm make the restored image as natural as its original version and the texture details of the image are also better protected.