基于匹配相容性的高效纹理合成

文中提出一种新的基于纹理块操作的纹理合成方法.对于每个纹理块,该方法根据样本纹理生成可与它进行拼接的纹理块邻接集合,并根据多步合成计算时纹理块之间的可拼接性(即匹配相容性)对邻接集合进行优化处理,即舍弃那些在多步操作后会引起匹配冲突的块.这样,合成计算时,可节省大量的关于邻接块之间的相似性度量计算,减少合成过程中的匹配冲突,由此提高纹理合成的效率.其合成质量,可与目前合成质量最好的纹理优化方法相媲美;而在合成速度方面,新方法的时间复杂度基本上是与目标纹理的大小成线性关系的,并经实验表明,新方法能以交互绘制的速度合成1024×1024像素的大纹理,而这是已有合成方法难以达到的.     

大尺寸纹理的实时合成

提出一种纹理合成方法,可实时高质量地生成大纹理.它先基于纹理特征变化的周期性分析,得到合适的纹理块尺寸,以使所划分的纹理块能高效反映这种周期性变化,便于生成高质量的纹理;然后,它在目标纹理上均衡地分布纹理块,使得垂直方向和水平方向上相邻的纹理块之间都留有一个块尺寸大小的空白区域,再对空白区域进行填充,以完成目标纹理的生成.显然,布块操作和填充操作均可并行地进行.同时,为每个纹理块预先生成可与其邻接匹配的纹理块集合,以便在填充计算时可用简便的集合求交计算来进行邻域约束的搜索,并将这种求交计算放在CPU中进行,而将邻接纹理块在重叠区域的缝合计算放到GPU中进行,以综合利用CPU和GPU的优势.实验表明,新方法可在一般微机上以45帧/秒的速度高质量地实时合成1024*1024的大纹理,而这是已有技术难以达到的.     

基于以像素块计算L2距离的块纹理合成

在基于MRF模型的块纹理合成中,计算两纹理块之间的L2距离和寻找最佳纹理匹配块都是非常耗时的过程。计算纹理块之间的L2距离是纹理合成中的瓶颈问题,制约着纹理合成的速度。针对合成速度问题提出一种新的基于以像素块计算两纹理块之间L2距离的块纹理合成方法。在计算两纹理块之间L2距离时不逐个像素点地进行计算,而将多个像素点视为一个像素点进行计算,极大地提高了计算两纹理块间L2距离的速度。该方法可以在几乎不影响图形合成质量的前提下,将纹理合成的速度提高数倍甚至更高。     

GPU适用的并行纹理合成算法

基于样图的纹理合成是一个大计算量过程,为了利用GPU的并行计算能力进行大规模纹理合成,我们提出一种并行纹理合成算法.该算法综合块查找和全局纹理优化算法分多遍进行纹理的合成和优化,其中每一遍分为串行纹理块定位和并行最优块匹配2个阶段.纹理块定位阶段在CPU端按照扫描线顺序确定待合成的邻域,并将邻域位置传入GPU;最优块匹配阶段在GPU端并行计算待合成邻域与对应样本邻域的全局距离,并查找出最优解得到匹配块.最后根据匹配过程统计数据自适应调整优化规模,在全局范围内对纹理进行迭代优化.实验结果表明,文中算法在保证大规模纹理合成效果的基础上减少了计算时间,能够满足交互式纹理合成的应用.     

图像的纹理标准性度量及其应用

通过分析纹理合成中子块参数对合成速度及质量的影响,发现对于一类纹理图像,不依赖于参数的选择即可快速高效地进行纹理合成。选择子块灰度平均值作为度量指标,根据纹理标准性强的图像应具有的特征,提出了一种新的计算图像纹理标准性系数的算法。结合大量计算结果,界定了一般图像、强标准性纹理以及弱标准性纹理的分类标准。并将其应用到纹理合成当中,对强标准性纹理图像的合成采用大尺度子块及零搜索的合成方法,提高了合成速度的同时保持合成质量不变。     

运用局部纹理映射加速曲面纹理合成

基于样图的纹理合成方法能够在网格曲面上合成高质量纹理,但合成速度有待进一步提高,对此提出一种运用局部纹理映射加速曲面纹理合成的算法.首先以三角形法向量、切向矢量与纹理尺度为约束,将三角网格曲面分割为一系列映射区与合成区;然后用基于块的平面纹理合成方法生成大面积样图,用调和映射方法对映射区进行纹理映射;最后采用基于三角块的合成方法生成合成区纹理.实验结果表明,该算法对一般的随机性纹理与半结构性纹理具有很高的合成质量,能够控制纹理方向与尺度的变化.由于大部分三角形的纹理通过局部纹理映射得到,仅需要合成少数三角形的纹理,纹理合成过程得到大幅度地加速.     

纹理合成的自相关性判别法及其应用

从加快纹理相似性的判别速度出发,提出了一种纹理合成的自相关性判别法.传统的纹理合成算法随着邻域和样本的增大,计算量将成倍增加,纹理合成速度减慢的劣势逐渐体现出来.因此,算法对样本纹理建立简单的自相关性距离查找表,利用L邻域内像素的自相关性距离作为像素匹配的判别依据,以查找取代传统匹配过程中的繁琐计算,极大地加快了合成速度,可实现动态的、多精度的合成效果调控,以及避免块匹配中易出现纹理接缝的问题.经验证,该算法可在纹理合成、图像修补及纹理检索中应用,并可很好地达到实时的应用要求.     

面向纹理合成的块尺寸自动选择算法

在现有的基于块的纹理合成算法中,针对块尺寸需要人工选择从而导致纹理合成质量不确定的问题,提出一种纹理合成中的块尺寸自动选择算法。在纹理样本上按扫描线顺序滑动子块直到遍历所有位置,对子块与纹理样本的直方图进行归一化与均值滤波预处理,然后计算二者直方图的交;在不同子块位置的上述计算结果中,取其最大值作为子块与样本的颜色相似度。针对颜色相似度与块尺寸的近似单调递增关系,采用二分法计算相似度阈值点所对应的横坐标,将其作为纹理合成的块尺寸。多种类型纹理的实验结果表明,该方法自动选择的块尺寸与最佳经验取值范围相吻合。所提方法不仅适用于结构性纹理的合成,而且适用于随机性纹理的合成,能够获得理想的合成结果。     

块纹理合成中纹理块尺寸自适应算法

分析了块纹理合成方法中纹理块尺寸对合成结果的影响,经过对大量不同类型纹理图像采样,归纳并验证了纹理相关性特征值与纹理块尺寸之间的关系,得出纹理相关性特征值自适应调整纹理块尺寸的算法.采用该算法可避免因纹理块尺寸选取不当而引起的合成时间的增加和合成质量的降低.     

纹理混合与纹理传输

基于样本的纹理合成方法是继纹理映射、过程纹理合成等方法后发展起来的一种纹理拼贴方法 ,而在已有的算法中 ,绝大多数只能处理单纹理样本的合成 .提出一种新颖的可处理两个纹理样本的纹理混合方法 ,通过调整参数可以控制纹理在合成结果中所占的比例 ,利用之字形双向扫描提高合成纹理的质量 ,即第一行先按扫描线顺序 ,第二行从行尾点开始按反方向进行扫描 ,第三行又按扫描线顺序 ,如此往复 ,直至图像结尾 ;还提出一种简单的纹理传输方法 ,用户可以根据实际需求进行设计和调整 ,获得各种结果 .计算一幅 3 0 0× 3 0 0的图片可在几秒内完成 .     

Synthesis of Color Textures for Multimedia Applications

An algorithm for synthesizing color textures from a small set of parameters is presented in this paper. The synthesis algorithm is based on the 2-D moving average model, and realistic textures resembling many real textures can be synthesized using this algorithm. A maximum likelihood estimation algorithm to estimate parameters from a sample texture is also presented. By combining the estimation and synthesis algorithms, a color texture can be synthesized from a sample texture without human intervention. Using the estimated parameters, a texture larger than the original image can be synthesized from a small texture sample. The synthesis algorithm does not require an expensive iterative algorithm, and the quality of synthesized textures may be acceptable for many multimedia applications. In the experiment, various textures suitable for multimedia applications are synthesized from parameters estimated from real textures.     

Accelerated parallel texture optimization

Texture optimization is a texture synthesis method that can efficiently reproduce various features of exemplar textures. However, its slow synthesis speed limits its usage in many interactive or real time applications. In this paper, we propose a parallel texture optimization algorithm to run on GPUs. In our algorithm, k-coherence search and principle component analysis （PCA） are used for hardware acceleration, and two acceleration techniques are further developed to speed up our GPU-based texture optimization. With a reasonable precomputation cost, the online synthesis speed of our algorithm is 4000＋ times faster than that of the original texture optimization algorithm and thus our algorithm is capable of interactive applications. The advantages of the new scheme are demonstrated by applying it to interactive editing of flow-guided synthesis.     

Texture Particles

This paper presents an analytical extension of texture synthesis techniques based on the distribution of elementary texture components. Our approach is similar to the bombing, cellular, macrostructured and lapped textures techniques, but provides the user with more control on both the texture analysis and synthesis phases. Therefore, high quality results can be obtained for a large number of structured or stochastic textures (bricks, marble, lawn, etc.). The analysis consists in decomposing textures into elementary components — that we call ``texture particles'' — and for which we analyze their specific spatial arrangements. The synthesis then consists in recomposing similar textures directly on arbitrary surfaces by taking into account the previously computed arrangements, extended to 3D surfaces. Compared to ``pixel‐based'' analysis and synthesis methods, which have been recently generalized to arbitrary surfaces, our approach has three major advantages: (1) it is fast, which allows the user to interactively control the synthesis process. This further allows us to propose a large number of tools, granting a high degree of artistic freedom to the user. (2) It avoids the visual deterioration of the texture components by preserving their shapes as well as their spatial arrangements. (3) The texture particles can be not only images, but also 3D geometric elements, which extends significantly the domain of application.     

Capture and Synthesis of 3D Surface Texture

We present and compare five approaches for capturing, synthesising and relighting real 3D surface textures. Unlike 2D texture synthesis techniques they allow the captured textures to be relit using illumination conditions that differ from those of the original. We adapted a texture quilting method due to Efros and combined this with five different relighting representations, comprising: a set of three photometric images; surface gradient and albedo maps; polynomial texture maps; and two eigen based representations using 3 and 6 base images.We used twelve real textures to perform quantitative tests on the relighting methods in isolation. We developed a qualitative test for the assessment of the complete synthesis systems. Ten observers were asked to rank the images obtained from the five methods using five real textures. Statistical tests were applied to the rankings.The six-base-image eigen method produced the best quantitative relighting results and in particular was better able to cope with specular surfaces. However, in the qualitative tests there were no significant performance differences detected between it and the other two top performers. Our conclusion is therefore that the cheaper gradient and three-base-image eigen methods should be used in preference, especially where the surfaces are Lambertian or near Lambertian.     

Analysis and Controlled Synthesis of Inhomogeneous Textures

Many interesting real‐world textures are inhomogeneous and/or anisotropic. An inhomogeneous texture is one where various visual properties exhibit significant changes across the texture's spatial domain. Examples include perceptible changes in surface color, lighting, local texture pattern and/or its apparent scale, and weathering effects, which may vary abruptly, or in a continuous fashion. An anisotropic texture is one where the local patterns exhibit a preferred orientation, which also may vary across the spatial domain. While many example‐based texture synthesis methods can be highly effective when synthesizing uniform (stationary) isotropic textures, synthesizing highly non‐uniform textures, or ones with spatially varying orientation, is a considerably more challenging task, which so far has remained underexplored. In this paper, we propose a new method for automatic analysis and controlled synthesis of such textures. Given an input texture exemplar, our method generates a source guidance map comprising: (i) a scalar progression channel that attempts to capture the low frequency spatial changes in color, lighting, and local pattern combined, and (ii) a direction field that captures the local dominant orientation of the texture. Having augmented the texture exemplar with this guidance map, users can exercise better control over the synthesized result by providing easily specified target guidance maps, which are used to constrain the synthesis process.     

Texture Stationarization: Turning Photos into Tileable Textures

Texture synthesis has grown into a mature field in computer graphics, allowing the synthesis of naturalistic textures and images from photographic exemplars. Surprisingly little work, however, has been dedicated to synthesizing tileable textures, that is, textures that when laid out in a regular grid of tiles form a homogeneous appearance suitable for use in memory‐sensitive real‐time graphics applications. One of the key challenges in doing so is that most natural input exemplars exhibit uneven spatial variations that, when tiled, show as repetitive patterns. We propose an approach to synthesize tileable textures while enforcing stationarity properties that effectively mask repetitions while maintaining the unique characteristics of the exemplar. We explore a number of alternative measures for texture stationarity and show how each measure can be integrated into a standard texture synthesis method (PatchMatch) to enforce stationarity at user‐controlled scales. We demonstrate the efficacy of our approach using a database of 118 exemplar images, both from publicly available sources as well as new ones captured under uncontrolled conditions, and we quantitatively analyze alternative stationarity measures for their robustness across many test runs using different random seeds. In conclusion, we suggest a novel synthesis approach that employs local histogram matching to reliably turn input photographs of natural surfaces into tiles well suited for artifact‐free tiling.     

带约束的基于样图地表纹理合成技术研究

在传统的视景仿真开发过程中,研究人员需要将海量的航空影像照片经过数字化等一系列处理变成可使用的地表纹理,映射在地形表面,生成地景数据库.此法耗费大量人力,而且地景库一经生成就不可再改变,真实性降低.提出一种新的地表纹理的生成方法,以随机生成的高度信息作为约束,不同的地表纹理单元作为样图,采用带约束的基于样图纹理合成算法合成出真实地表纹理图像.实验中,通过改变高程约束和对多种地表纹理进行组合,可以方便快速地合成高质量的二维地表纹理图像.提出的纹理合成的方法,为今后与数字地图相结合开发地景数据库,积累一定的经验.对提高视景数据库的真实性具有实际意义.     

Texture synthesis via the matching compatibility between patches

A new patch-based texture synthesis method is presented in this paper. By the method, a set of patches that can be matched with a sampled patch for growing textures effectively, called the matching compatibility between patches, is generated first for each patch, and the set is further optimized by culling the patches that may cause synthesis conflicts. In this way, similarity measurement calculation for selecting suitable patches in texture synthesis can be greatly saved, and synthesis conflicts between neighbouring patches are substantially reduced. Furthermore, retrace computation is integrated in the synthesis process to improve the texture quality. As a result, the new method can produce high quality textures as texture optimization, the best method to date for producing good textures, and run in a time complexity linear to the size of the output texture. Experimental results show that the new method can interactively generate a large texture in 1024 × 1024 pixels, which is very difficult to achieve by existing methods. Supported by the National Basic Research Program of China (Grant No. 2009CB320802), the National Natural Science Foundation of China (Grant Nos. 60773026, 60833007), the National High-Tech Research & Development Program of China (Grant Nos. 2006AA01Z306, 2008AA01Z301), and the Research Grant of University of Macau