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首页> 外文期刊>International journal of communication systems >Modified pyramid dual tree direction filter‐based image denoising via curvature scale and nonlocal mean multigrade remnant filter
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Modified pyramid dual tree direction filter‐based image denoising via curvature scale and nonlocal mean multigrade remnant filter

机译：通过曲率标度和非局部均值多级残差滤波器对基于金字塔双树方向滤波器的图像进行去噪

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摘要

To alleviate the disadvantage of traditional image denoising method in big images data, we propose a modified pyramid dual tree direction filter with nonlocal mean multigrade remnant filter for image denoising in this paper. The proposed denoising method is partitioned into 4 processes. Firstly, curvature scale model is used for building pyramid dual tree direction filter coefficients of noised image. Additionally, the coefficients are calculated by robust Bayes least square method. Then, we use pyramid dual tree direction filter inverse transformation to reconstruct an initial denoised image. At last, nonlocal mean multigrade remnant filter is adopted to filter the initial denoised image and we obtain the final denoised image. The proposed method completely used the multiscale and multidirectional selectivity with approximately translation invariance of pyramid dual tree direction filter. Finally, we assess the image denoising performance of the proposed approach over several test images and compare our results with the state-of-the-art denoising algorithms. Our experiments show that the proposed image denoising method achieves better results than other methods. Furthermore, our new method not only effectively removes the noise but also better keeps the edge and detail information of texture and structure.
机译：为了缓解大图像数据中传统图像去噪方法的缺点，本文提出了一种改进的金字塔非对称双树方向滤波器，其具有非局部均值多级余数滤波器，用于图像去噪。所提出的去噪方法被分为四个过程。首先，利用曲率尺度模型建立噪声图像的金字塔对偶树方向滤波系数。另外，通过鲁棒贝叶斯最小二乘法计算系数。然后，我们使用金字塔对偶树方向滤波器逆变换来重建初始去噪图像。最后，采用非局部均值多级残差滤波器对初始去噪图像进行滤波，得到最终去噪图像。所提出的方法完全利用了金字塔双树方向滤波器近似平移不变性的多尺度和多方向选择性。最后，我们在几种测试图像上评估该方法的图像去噪性能，并将我们的结果与最新的去噪算法进行比较。我们的实验表明，所提出的图像去噪方法比其他方法取得了更好的效果。此外，我们的新方法不仅可以有效消除噪点，而且可以更好地保留纹理和结构的边缘和细节信息。

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