A bound optimization approach to wavelet-based image deconvolution
A bound optimization approach to wavelet-based image deconvolution
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DOI：
10.1109/icip.2005.1530172
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发表时间：
2005-11
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通讯作者：
Mário A. T. Figueiredo;Robert D. Nowak
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文献类型：
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作者：
Mário A. T. Figueiredo;Robert D. Nowak
我们解决了在小波域中表达的 I/sub p/范数（和其他）惩罚下的图像反卷积问题。我们提出了一种基于边界优化方法的算法；这种方法允许在不使用丢失/隐藏数据的概念的情况下导出 EM 类型算法。该算法通过正交或冗余小波变换都具有可证明的单调性。我们还推导出 l/sub p/ 范数惩罚的界限，以获得任何 p /spl isin/ [0, 2] 的封闭形式更新方程。实验结果表明，所提出的方法实现了最先进的性能。
We address the problem of image deconvolution under I/sub p/ norm (and other) penalties expressed in the wavelet domain. We propose an algorithm based on the bound optimization approach; this approach allows deriving EM-type algorithms without using the concept of missing/hidden data. The algorithm has provable monotonicity both with orthogonal or redundant wavelet transforms. We also derive bounds on the l/sub p/ norm penalties to obtain closed form update equations for any p /spl isin/ [0, 2]. Experimental results show that the proposed method achieves state-of-the-art performance.