IEEE Trans Image Process. 2014 Jun;23(6):2651-62. doi: 10.1109/TIP.2014.2317985. Epub 2014 Apr 17.
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.
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，并且具有重尾。一种典型的混合噪声是加性白高斯噪声（AWGN）和脉冲噪声（IN）的混合。许多混合噪声去除方法都是基于检测的方法。它们首先检测 IN 像素的位置，然后去除混合噪声。然而，当混合噪声较强时，这些方法往往会产生许多伪影。本文提出了一种简单而有效的方法，即加权编码与稀疏非局部正则化（WESNR），用于混合噪声去除。在 WESNR 中，没有明确的脉冲像素检测步骤；相反，通过加权编码进行软脉冲像素检测，同时处理 IN 和 AWGN。同时，图像稀疏先验和非局部自相似性先验被集成到一个正则化项中，并引入到变分编码框架中。实验结果表明，所提出的 WESNR 方法在定量度量和视觉质量方面都取得了领先的混合噪声去除性能。
