Efficient Graph Construction For Image Representation
Efficient Graph Construction For Image Representation
复制标题
用于图像表示的高效图形构建
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Antonio Ortega
中科院分区：
文献类型：
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作者：
Sarath Shekkizhar;Antonio Ortega
图对于解释广泛使用的图像处理方法（例如双边滤波）或开发新的方法（例如基于内核的技术）很有用。然而，经常使用简单的图结构，其中边权重和连接性取决于几个参数。特别是，图的稀疏性是由窗口大小的选择决定的。作为替代方案，我们扩展并适应最近引入的非负核回归（NNK）图构造的图像。在 NNK 图中，稀疏性适应内在的数据属性。此外，虽然之前的工作考虑了通用设置中的 NNK 图，但在这里我们开发了利用图像属性的新颖算法，以便 NNK 方法可以扩展到大图像。我们的实验表明，与使用直接从双边滤波器导出的图相比，稀疏 NNK 图实现了改进的能量压缩和去噪性能。
Graphs are useful to interpret widely used image processing methods, e.g., bilateral filtering, or to develop new ones, e.g., kernel based techniques. However, simple graph constructions are often used, where edge weight and connectivity depend on a few parameters. In particular, the sparsity of the graph is determined by the choice of a window size. As an alternative, we extend and adapt to images recently introduced non negative kernel regression (NNK) graph construction. In NNK graphs sparsity adapts to intrinsic data properties. Moreover, while previous work considered NNK graphs in generic settings, here we develop novel algorithms that take advantage of image properties, so that the NNK approach can scale to large images. Our experiments show that sparse NNK graphs achieve improved energy compaction and denoising performance when compared to using graphs directly derived from the bilateral filter.