基于整体变分法的数字图像修复技术研究

【作者】 周密；

【导师】 彭进业；

【作者基本信息】 西北大学 ， 信号与信息处理， 2008， 硕士

【摘要】 数字图像修复是指对数字图像中丢失或破损的部分进行还原修复的过程。通过对破损的图像进行修复更新,使得图像有更好的视觉效果,达到以假乱真的目的。目前,数字图像修复技术在图像压缩、图像编码、文物考古、军事保密等方面有着极为重要的应用。文章对现有数字图像修复的扩散技术、匹配技术和图像分解技术的发展做了说明,并对其中有代表意义的算法,比如—BSCB模型算法、Criminisi的图像分解法等算法进行原理分析。本文重点以整体变分法和纹理匹配法作为扩散技术、匹配技术的代表进行研究,并在原有算法的基础上作了一定改进。在整体变分法中加入了相关度系数这一概念,使图像在扩散修复的同时兼顾了各个参考点对目标点不同的影响程度;在纹理匹配法中,对纹理模板块大小和匹配块搜索范围进行了改进说明。两种改进算法在实验中取得了不错的修复效果。改进后的整体变分法修复后的图像过渡自然,视觉效果有了比较大提高;采用规定了模板大小和匹配块搜索范围的纹理匹配法修复后的图像与周围图像融合的更加真实,而且修复所需时间明显缩短。本文同时对两种算法各自的适用范围进行了概括。在图像信息丢失较少或者图像边缘信息缺失轻微的情况下,整体变分算法利用边缘扩散技术使得图像更加的平滑;在图像信息丢失较多或者边缘信息缺失严重的情况下,纹理匹配算法利用剩余图像中的相似纹理信息填充修复使得图像更加自然,纹理细节保护的比较好。更多还原

【Abstract】 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.更多还原