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首页> 外文期刊>Optik: Zeitschrift fur Licht- und Elektronenoptik: = Journal for Light-and Electronoptic >Image denoising using multivariate model in shiftable complex directional pyramid domain and principal neighborhood dictionary in spatial domain
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Image denoising using multivariate model in shiftable complex directional pyramid domain and principal neighborhood dictionary in spatial domain

机译：移位复方向金字塔域中使用多元模型的图像去噪和空间域中的主邻域字典

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摘要

The major challenge for image denoising is how to effectively remove the noise and preserve the detail information to get better visual quality and higher peak signal-to-noise ratio (PSNR). A new image denoising methods based on combination of multivariate shrinkage model in shiftable complex directional pyramid (PDTDFB) domain and principal neighborhood dictionary (PND) non-local means algorithm in spatial domain is proposed. In PDTDFB domain, the PDTDFB coefficients are modeled as multivariate non-Gaussian distribution taking into account the interscale and intrascale dependency correlation. Then a multivariate shrinkage function is derived by the maximum a posterior (MAP) estimator and the denoised coefficients are obtained. Although the PDTDFB-based algorithm achieves efficient denoising result, it is prone to producing salient artifacts which relate to the structure of the PDTDFB. Principal neighborhood dictionary (PND) is further employed to alleviate the artifacts with small computational load in spatial domain. Experimental results indicate that the proposed method is competitive with other excellent denoising methods in terms of PSNR value and visual quality. (C) 2015 Published by Elsevier GmbH.
机译：图像去噪的主要挑战是如何有效消除噪声并保留细节信息，以获得更好的视觉质量和更高的峰值信噪比（PSNR）。提出了一种基于可移动复杂定向金字塔（PDTDFB）域多元收缩模型和空间域主邻域字典（PND）非局部均值算法相结合的图像去噪新方法。在PDTDFB域中，考虑到尺度间和尺度内相关性相关性，将PDTDFB系数建模为多元非高斯分布。然后，通过最大后验（MAP）估计量得出多元收缩函数，并获得去噪系数。尽管基于PDTDFB的算法实现了高效的去噪效果，但它易于产生与PDTDFB的结构有关的显着伪像。进一步采用主邻域字典（PND）来减轻空间域中计算量较小的伪影。实验结果表明，该方法在PSNR值和视觉质量方面与其他出色的降噪方法相比具有竞争优势。 （C）2015由Elsevier GmbH发布。

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