Which wavelet bases are the best for image denoising?
Which wavelet bases are the best for image denoising?
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哪些小波基最适合图像去噪？
DOI：
10.1117/12.614999
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发表时间：
2005
影响因子：
1.6
通讯作者：
M. Unser
中科院分区：
文献类型：
--
作者：
F. Luisier;T. Blu;B. Forster;M. Unser
我们使用了一组非冗余的正交小波变换，并在每个单独的小波子带上应用了一种称为SURE收缩的去噪方法来对被加性高斯白噪声污染的图像进行去噪。结果表明，与标准小波基(Daubechies小波、symlet和coiflet)相比，对于不同的图像和较大的输入噪声水平，正交分数(α，τ)-B样条基具有最佳的峰值信噪比。此外，最佳集合(α，τ)的选择可以在MSE估计(当然)本身上执行，而不是在实际的MSE(甲骨文)上执行。最后，复值分数B-样条的使用带来了更显著的改进；它们的性能也优于复数Daubechies小波。
We use a comprehensive set of non-redundant orthogonal wavelet transforms and apply a denoising method called SUREshrink in each individual wavelet subband to denoise images corrupted by additive Gaussian white noise. We show that, for various images and a wide range of input noise levels, the orthogonal fractional (α, τ)-B-splines give the best peak signal-to-noise ratio (PSNR), as compared to standard wavelet bases (Daubechies wavelets, symlets and coiflets). Moreover, the selection of the best set (α, τ) can be performed on the MSE estimate (SURE) itself, not on the actual MSE (Oracle). Finally, the use of complex-valued fractional B-splines leads to even more significant improvements; they also outperform the complex Daubechies wavelets.