论文标题
将BM3D与定向小波包的耦合图像denoing
Coupling BM3D with directional wavelet packets for image denoising
论文作者
论文摘要
本文通过将基于定向准分析小波数据包（QWP）与流行的BM3D算法相结合的方法来提出图像降级算法。 QWP及其相应的转换在[1]中设计。 denoising算法QWP（QWPDN）使用双变量收缩方法将自适应局部软阈值应用于变换系数。组合方法由QWPDN和BM3D算法的几个迭代组成，其中一种算法的输出将输入更新到另一个算法（交叉增强）。QWPDN和BM3D方法相互补充。捕获边缘和精细纹理模式的QWPDN功能与BM3D中固有的图像中的贴片和贴片的自相似性相结合。获得的结果与最新的最新算法相当有竞争力。我们将组合方法的性能与使用方向帧和BM3D算法的CPTTP-CTF6，DAS-2算法的性能进行了比较。在绝大多数实验中，组合算法的表现优于上述方法。
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.
