Mixed Noise Removal by Weighted Encoding With Sparse Nonlocal Regularization
Mixed Noise Removal by Weighted Encoding With Sparse Nonlocal Regularization
复制标题
通过稀疏非局部正则化加权编码消除混合噪声
DOI：
10.1109/tip.2014.2317985
复制
发表时间：
2014-06-01
影响因子：
10.6
通讯作者：
Yang, Jian
中科院分区：
文献类型：
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作者：
Jiang, Jielin;Zhang, Lei;Yang, Jian
从自然图像中去除混合噪声是一项具有挑战性的任务，因为噪声分布通常没有参数模型并且具有厚尾。一种典型的混合噪声是与脉冲噪声（IN）耦合的加性白色高斯噪声（AWGN）。许多混合噪声去除方法是基于检测的方法。它们首先检测IN像素的位置，然后去除混合噪声。然而，当混合噪声很强时，这样的方法往往会产生许多伪像。在本文中，我们提出了一个简单而有效的方法，即加权编码与稀疏非局部正则化（WESNR），混合噪声去除。在WESNR中，没有明确的脉冲像素检测步骤，而是通过加权编码的软脉冲像素检测来同时处理IN和AWGN。同时，图像稀疏性先验和非局部自相似性先验被集成为一个正则化项，并引入到变分编码框架。实验结果表明，所提出的WESNR方法实现了领先的混合噪声去除性能的定量措施和视觉质量。
Mixed noise removal from natural images is a challenging task since the noise distribution usually does not have a parametric model and has a heavy tail. One typical kind of mixed noise is additive white Gaussian noise (AWGN) coupled with impulse noise (IN). Many mixed noise removal methods are detection based methods. They first detect the locations of IN pixels and then remove the mixed noise. However, such methods tend to generate many artifacts when the mixed noise is strong. In this paper, we propose a simple yet effective method, namely weighted encoding with sparse nonlocal regularization (WESNR), for mixed noise removal. In WESNR, there is not an explicit step of impulse pixel detection; instead, soft impulse pixel detection via weighted encoding is used to deal with IN and AWGN simultaneously. Meanwhile, the image sparsity prior and nonlocal self-similarity prior are integrated into a regularization term and introduced into the variational encoding framework. Experimental results show that the proposed WESNR method achieves leading mixed noise removal performance in terms of both quantitative measures and visual quality.