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首页> 外文期刊>Signal processing >Poisson Wiener filtering with non-local weighted parameter estimation using stochastic distances
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Poisson Wiener filtering with non-local weighted parameter estimation using stochastic distances

机译：具有随机距离的非局部加权参数估计的泊松维纳滤波

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Highlights•A novel Wiener filtering approach for Poisson corrupted images is proposed.•The proposed method relies on a non-local neighborhood for parameter estimation.•Stochastic distances are employed as similarity metrics between image patches.•Better trade-off between noise smoothing and the preservation of image features.•The proposed approach can be extended to other noise models and non-local filters.AbstractThe Wiener filter is a classical approach for signal denoising and it is known to be the optimum filter in the sense of the linear minimum mean square error (LMMSE). In general, knowledge of some statistical parameters of the underlying signal and noise are required to compute the Wiener filter. These parameters are usually estimated from the corrupted image using a local neighborhood, thus assuming that the signal and noise are locally stationary. However, images corrupted by signal-dependent noise, such as Poisson noise, are not locally stationary. In this paper, we are proposing a novel Wiener filtering approach for Poisson corrupted images considering a non-local weighted parameter estimation. In the proposed method, named Poisson Non-Local Wiener filter (PNL-Wiener), filtering parameters are estimated from the degraded image considering a non-local neighborhood, where the weights of the estimation function are computed based on the stochastic distances between image patches. Experimental results show that the proposed method is competitive to otherstate-of-the-artdenoising methods designed specifically for Poisson corrupted images, yet providing better preservation of edges and fine details in the images.
机译： 突出显示 • 提出了一种新颖的针对Poisson损坏图像的Wiener过滤方法。 • 建议的方法依赖于非局部邻域进行参数估计。 • 随机距离 • 投注在噪声平滑和图像特征保留之间进行权衡。 • 建议的方法可以扩展到其他噪声模型和非本地滤波器。 摘要 维纳滤波器是用于信号降噪的经典方法从线性最小均方误差（LMMSE）的角度来看，它是最佳滤波器。通常，需要基础信号和噪声的一些统计参数的知识来计算维纳滤波器。通常使用局部邻域从损坏的图像中估计这些参数，因此假设信号和噪声是局部静止的。但是，被依赖信号的噪声（例如泊松噪声）破坏的图像不是局部静止的。在本文中，我们提出一种考虑非局部加权参数估计的新颖的针对Poisson损坏图像的Wiener滤波方法。在所提出的名为泊松非局部维纳滤波器（PNL-Wiener）的方法中，考虑到非局部邻域，从降级图像中估计出滤波参数，其中，基于图像块之间的随机距离来计算估计函数的权重。实验结果表明，所提出的方法与专门为Poisson损坏图像设计的其他最新技术降噪方法相比具有竞争优势，但可以更好地保留图像的边缘和精细细节。

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