Structure-Preserving Image Smoothing via Region Covariances
Structure-Preserving Image Smoothing via Region Covariances
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DOI：
10.1145/2508363.2508403
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发表时间：
2013-11-01
影响因子：
6.2
通讯作者：
Erdem, Aykut
中科院分区：
文献类型：
--
作者：
Karacan, Levent;Erdem, Erkut;Erdem, Aykut
近年来，出现了新的图像平滑技术，提供了新的见解，并提出了新的问题，这个充分研究的问题的性质。具体地说，这些模型通过利用基于非梯度的边缘定义或区分边缘与振荡的特殊措施，将给定图像分成其结构层和纹理层。在这项研究中，我们提出了一种替代的但简单的图像平滑方法，它依赖于简单的图像特征的协方差矩阵，又名区域协方差。使用二阶统计作为补丁描述符，使我们能够隐式地捕捉局部结构和纹理信息，使我们的方法特别有效的结构提取纹理。我们的实验结果表明，所提出的方法导致更好的图像分解相比，国家的最先进的方法，并保持突出的边缘和阴影。此外，我们还证明了我们的方法在一些图像编辑和操作任务，如图像抽象，纹理和细节增强，图像合成，逆半色调和接缝雕刻的适用性。
Recent years have witnessed the emergence of new image smoothing techniques which have provided new insights and raised new questions about the nature of this well-studied problem. Specifically, these models separate a given image into its structure and texture layers by utilizing non-gradient based definitions for edges or special measures that distinguish edges from oscillations. In this study, we propose an alternative yet simple image smoothing approach which depends on covariance matrices of simple image features, aka the region covariances. The use of second order statistics as a patch descriptor allows us to implicitly capture local structure and texture information and makes our approach particularly effective for structure extraction from texture. Our experimental results have shown that the proposed approach leads to better image decompositions as compared to the state-of-the-art methods and preserves prominent edges and shading well. Moreover, we also demonstrate the applicability of our approach on some image editing and manipulation tasks such as image abstraction, texture and detail enhancement, image composition, inverse halftoning and seam carving.