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arXiv 2008.11595eess.IV
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耦合BM3D和方向小波包的图像去噪

Coupling BM3D with directional wavelet packets for image denoising

Amir Averbuch, Pekka Neittaanmaki, Valery Zheludev, Moshe Salhov, Jonathan Hauser

备注：
The new paper "Cross-boosting of WNNM Image Denoising method by Directional Wavelet Packets" to be submitted has some overlap with the paper "Coupling BM3D with directional wavelet packets for image denoising"
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摘要：

提出了一种基于方向准解析小波包（qWPs）的图像去噪方法，并将其与目前流行的BM 3D算法相结合，实现了一种图像去噪算法。在[1]中设计了qWP及其相应的变换。去噪算法qWP（qWPdn）使用双变量收缩方法对变换系数应用自适应局部软阈值。组合方法由qWPdn和BM 3D算法的多次迭代组成，其中一个算法的输出更新另一个算法的输入（交叉提升）JWPdn和BM 3D方法相互补充。qWPdn捕获边缘和精细纹理图案的能力与利用真实图像中的稀疏性和图像中的补丁的自相似性相结合，这是BM 3D中固有的。所得到的结果是相当有竞争力的最好的国家的最先进的算法。我们比较的性能与性能的cptTP-CTF 6，DAS-2算法，它使用定向帧，和BM 3D算法的组合方法。在绝大多数实验中，组合算法优于上述方法。

英文摘要：

The paper presents an image denoising algorithm by combining a method that is based on directional quasi-analytic wavelet packets (qWPs) with the popular BM3D algorithm. The qWPs and its corresponding transforms are designed in [1]. The denoising algorithm qWP (qWPdn) applies an adaptive localized soft thresholding to the transform coefficients using the Bivariate Shrinkage methodology. The combined method consists of several iterations of qWPdn and BM3D algorithms, where the output from one algorithm updates the input to the other (cross-boosting).The qWPdn and BM3D methods complement each other. The qWPdn capabilities to capture edges and fine texture patterns are coupled with utilizing the sparsity in real images and self-similarity of patches in the image that is inherent in the BM3D. The obtained results are quite competitive with the best state-of-the-art algorithms. We compare the performance of the combined methodology with the performances of cptTP-CTF6, DAS-2 algorithms, which use directional frames, and the BM3D algorithm. In the overwhelming majority of the experiments, the combined algorithm outperformed the above methods.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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