Research On Group Marching Method Based Digital Image Inpainting Technique

Image restoration is one of the important research hotspots in computer graphics, computer vision and image processing. It has great potential in many applications, especially in restoration of art, fast data transferring on the Internet, computer automation, special effects, virtual reality and so on.Image inpainting is an important research topic in the area of image restoration; its objective is to restore the lost information according to around image information. Although the inpainting basics are straightforward, most inpainting techniques published in the literature are complex to understand and implement. Fast marching method (FMM) is an efficient algorithm for level set applications whose total computation cost is O(NlbN). Kim presented a more efficient algorithm called group marching method (GMM) with the complexity of O(N). Motivated by his work, we propose a new technique for image inpainting based on GMM.The work includes important aspects as follows:A GMM based non-texture inpainting technique is presented. This technique is especially useful for inpainting linear regions on non-texture images. Compared to other techniques, the technique we proposed is much faster while preserving almost the same inpaint result.A GMM based texture inpainting technique is presented. This technique is good at inpainting images contain both texture and structure information, and can also be used for object removal. For texture images inpainting, we introduce GMM for narrow band evolution. It seems that our technique can propagate original image's structure and texture infomation very well.We introduce GMM for narrow band evolution. Since it takes much less time on narrow band evolution, our technique is more efficient.Three experiments using our algorithm are illustrated, that is, linear regions inpainting on non-texture images, object removal, and texture image inpainting. It seems that our technique is much faster than other inpaint techniques while preserving almost the same result.