论文标题
图像分割中的空间自适应正则化
Spatially Adaptive Regularization in Image Segmentation
论文作者
论文摘要
我们通过引入考虑空间图像信息的局部正则化，修改了Chan，Esedoglu和Nikolova提出的总变量调查的图像分割模型[Siam on Applied Mathematics 66，2006]。我们提出了一些针对给定图像的卡通质量分解，在平均值和中位过滤器以及阈值技术上定义局部正规化参数的技术，目的是防止分段构造或平滑区域的过度正则化，并在非牙齿区域中保持空间特征。我们通过使用拆分布雷格曼迭代来解决修改模型。数值实验显示了我们方法的有效性。
We modify the total-variation-regularized image segmentation model proposed by Chan, Esedoglu and Nikolova [SIAM Journal on Applied Mathematics 66, 2006] by introducing local regularization that takes into account spatial image information. We propose some techniques for defining local regularization parameters, based on the cartoon-texture decomposition of the given image, on the mean and median filters, and on a thresholding technique, with the aim of preventing excessive regularization in piecewise-constant or smooth regions and preserving spatial features in nonsmooth regions. We solve the modified model by using split Bregman iterations. Numerical experiments show the effectiveness of our approach.
