基于拉普拉斯变换域局部参数的图像去噪

V. K. Nath, A. Mahanta
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引用次数: 9

摘要

本文提出了一种基于ltp系数统计建模的图像去噪方法。首先将叠置的变换系数重组为类小波结构,然后将重组后的系数子带统计量以类似于小波系数的方式建模。我们建议使用具有局部方差的拉普拉斯概率密度函数(pdf)来模拟子带中重排的LT系数。这种简单的分布很好地模拟了叠接变换系数的局部性和重尾性。利用具有局部方差的拉普拉斯概率密度函数(pdf)的最大后验估计器来估计无噪声的重叠变换系数。实验结果表明,所提出的低复杂度图像去噪方法优于几种基于小波的图像去噪技术,也优于现有的两种基于LT的图像去噪方案。我们在本文中的主要贡献是使用局部拉普拉斯先验对LT系数进行统计建模,并使用MAP估计程序与该提出的先验恢复噪声图像的LT系数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image denoising based on Laplace distribution with local parameters in Lapped Transform domain
In this paper, we present a new image denoising method based on statistical modeling of Lapped Transform (LT) coefficients. The lapped transform coefficients are first rearranged into wavelet like structure, then the rearranged coefficient subband statistics are modeled in a similar way like wavelet coefficients. We propose to model the rearranged LT coefficients in a subband using Laplace probability density function (pdf) with local variance. This simple distribution is well able to model the locality and the heavy tailed property of lapped transform coefficients. A maximum a posteriori (MAP) estimator using the Laplace probability density function (pdf) with local variance is used for the estimation of noise free lapped transform coefficients. Experimental results show that the proposed low complexity image denoising method outperforms several wavelet based image denoising techniques and also outperforms two existing LT based image denoising schemes. Our main contribution in this paper is to use the local Laplace prior for statistical modeling of LT coefficients and to use MAP estimation procedure with this proposed prior to restore the noisy image LT coefficients.
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