【作者】 沈强；

【导师】 李伟青；

【作者基本信息】 浙江大学 ， 计算机应用， 2005， 硕士

【摘要】 纹理合成是计算机图形学、计算机视觉中的一个研究热点，具有广泛的应用价值。近年出现的基于样图的纹理合成算法将纹理分成小块，将其描述为一个二维的马尔可夫模型，待合成的区域由其周围己合成的区域决定，通过相邻块的重叠区域在样图中搜索到最匹配的块作为当前块。 传统的单纯基于颜色的匹配算法在处理纹理时往往效果不尽如人意，通过试验，我们发现，基于样图的纹理合成算法大部分时候是一个少约束的问题，往往对于一个块，在样图中能找到数个匹配度相似但内容相差较大的块，这样往往选中的块不能和已有的块结合得很好。为了解决这个问题，我们提出了一种新的同时考虑颜色差和结构性信息的差别的图像匹配算法。本文提出的算法首先对图像进行边缘检测，将检测到的边缘点作为结构性信息。传统的点点匹配算法具有指数级的复杂度，很难直接用于纹理匹配。为此，我们提出了一个两层的匹配算法，大大降低了匹配的复杂度。算法首先将相邻的边界点连成曲线，先在曲线级别做匹配，在曲线级别匹配好之后，再在每个匹配的曲线点集上进行逐点匹配，两个图像的结构性差别为对应点的差别的和。试验证明，该算法能找到更精确的匹配块，进而增强纹理合成的效果。 本文还将本算法应用于图像修复和超分辨率技术，实验表明，本文提出的算法能提高已有算法的修复和合成效果。更多还原

【Abstract】 Texture synthesis is a very hot research area in both computer graphics and computer vision. Recently a new sample-based algorithm is proposed. First it divides texture into many small patches, which could be described with a 2-D Markov model, and the patch to be synthesized is determined by the information of its neighbors which are already synthesized. The best-match patch is searched as the current patch by the overlapped region of adjacent patches in the sample.The traditional matching algorithms which are only dependent on color information cannot capture the structural difference of two images, thus often give bad results. Through a lot of experiments, we find that the sample-based texture synthesis algorithm turns to an under-constrained problem at most of the time. For a given patch, we can always find several patches have similar matching score but quite different content information, so the selected patch from sample is usually unreasonable for it could not keep good coherence with existing patches. In this paper, we present a novel algorithm to solve this problem. It takes into account of the color-difference and structure-difference information at the same time. First, the structure feature is extracted. For the traditional point-by-point matching algorithm takes exponential complexity, it’s difficult to be used directly. This problem is overcome by a new two-level matching algorithm also presented in this paper. Thus the matching complexity is greatly reduced. First, the boundary points are connected into curves. After the curves are matched, the point-to-point matching is employed between matched curves. The structure-difference is regarded as the sum of the difference between the matched points. Experiments show that the presented method produces more reasonable and credible synthesis results for the improved matching algorithm.Furthermore, we find that the matching algorithm is also applicable in image inpainting and super-resolution sampling and produces good results.更多还原