Denoising through wavelet shrinkage: an empirical study
Denoising through wavelet shrinkage: an empirical study
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DOI：
10.1117/1.1525793
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发表时间：
2003-01-01
影响因子：
1.1
通讯作者：
Kamath, C
中科院分区：
文献类型：
--
作者：
Fodor, IK;Kamath, C
基于小波系数阈值化的技术在去噪数据中越来越受欢迎。其思想是将数据转换为小波基，其中“大”系数主要是信号，“小”系数代表噪声。通过适当地修改这些系数，可以从数据中去除噪声。我们评估了几个2-D去噪程序使用加性高斯噪声损坏的测试图像。我们考虑这些技术的全局、电平依赖和子带依赖实现。我们的研究结果，使用均方误差作为衡量质量的去噪，表明SureShrink和BayesShrihk方法始终优于其他基于小波的技术。相比之下，我们发现，简单的空间滤波器的组合导致图像更粗糙，边缘更平滑，尽管误差小于基于小波的方法。(C)2003 SPIE和IST。
Techniques based on thresholding of wavelet coefficients are gaining popularity for denoising data. The idea is to transform the data into the wavelet basis, where the "large" coefficients are mainly the signal, and the "smaller" ones represent the noise. By suitably modifying these coefficients,. the noise can be removed from the data. We evaluate several 2-D denoising procedures using test images corrupted with additive Gaussian noise. We consider global, level-dependent, and subband-dependent implementations of these techniques., Our results, using the mean squared error as a measure of the quality of denoising, show that the SureShrink and the BayesShrihk methods consistently outperform the other wavelet-based techniques. In contrast, we found that a combination of simple spatial filters lead to images that were grainier with smoother edges, though the error was smaller than in the wavelet-based methods. (C) 2003 SPIE and IST.