Three Novel Edge Detection Methods for Incomplete and Noisy Spectral Data
Three Novel Edge Detection Methods for Incomplete and Noisy Spectral Data
复制标题
针对不完整和噪声光谱数据的三种新颖边缘检测方法
DOI：
10.1007/s00041-008-9038-9
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发表时间：
2008
影响因子：
1.2
通讯作者：
Jing Zou
中科院分区：
文献类型：
--
作者：
E. Tadmor;Jing Zou
我们提出了三种新的方法来恢复边缘的分段光滑函数从他们可能不完整和嘈杂的光谱信息。所提出的方法利用三种不同的方法：#1。基于随机的稀疏快速傅立叶逆变换（sIFT）; #2.基于总变差（TV）的压缩感测;以及#3。修改过的零交叉。不同的方法有一个共同的功能：通过尺度分离来识别边缘。为此，我们主张在这里使用浓度内核（塔德莫尔，学报编号。16：305-378，2007），以将全局光谱数据转换成近似跳跃函数，该近似跳跃函数定位在边缘的紧邻邻域中。建立在这些浓度内核，我们表明，sIFT方法，基于电视的压缩感知和零交叉产生有效的边缘检测器，其中许多跳跃不连续准确地恢复。一维和二维的数值结果。
We propose three novel methods for recovering edges in piecewise smooth functions from their possibly incomplete and noisy spectral information. The proposed methods utilize three different approaches: #1. The randomly-based sparse Inverse Fast Fourier Transform (sIFT); #2. The Total Variation-based (TV) compressed sensing; and #3. The modified zero crossing. The different approaches share a common feature: edges are identified through separation of scales. To this end, we advocate here the use ofconcentration kernels(Tadmor, Acta Numer. 16:305–378, 2007), to convert the global spectral data into an approximate jump function which is localized in the immediate neighborhoods of the edges. Building on these concentration kernels, we show that the sIFT method, the TV-based compressed sensing and the zero crossing yield effective edge detectors, where finitely many jump discontinuities are accurately recovered. One- and two-dimensional numerical results are presented.