State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China.
IEEE Trans Image Process. 2012 Apr;21(4):1663-75. doi: 10.1109/TIP.2011.2172804. Epub 2011 Oct 19.
Impulse noise detection is a critical issue when removing impulse noise and impulse/Gaussian mixed noise. In this paper, we propose a new detection mechanism for universal noise and a universal noise-filtering framework based on the nonlocal means (NL-means). The operation is carried out in two stages, i.e., detection followed by filtering. For detection, first, we propose the robust outlyingness ratio (ROR) for measuring how impulselike each pixel is, and then all the pixels are divided into four clusters according to the ROR values. Second, different decision rules are used to detect the impulse noise based on the absolute deviation to the median in each cluster. In order to make the detection results more accurate and more robust, the from-coarse-to-fine strategy and the iterative framework are used. In addition, the detection procedure consists of two stages, i.e., the coarse and fine detection stages. For filtering, the NL-means are extended to the impulse noise by introducing a reference image. Then, a universal denoising framework is proposed by combining the new detection mechanism with the NL-means (ROR-NLM). Finally, extensive simulation results show that the proposed noise detector is superior to most existing detectors, and the ROR-NLM produces excellent results and outperforms most existing filters for different noise models. Unlike most of the other impulse noise filters, the proposed ROR-NLM also achieves high peak signal-to-noise ratio and great image quality by efficiently removing impulse/Gaussian mixed noise.
脉冲噪声检测是去除脉冲噪声和脉冲/高斯混合噪声的关键问题。在本文中，我们提出了一种新的通用噪声检测机制和基于非局部均值（NL-means）的通用噪声滤波框架。该操作分两个阶段进行，即检测和滤波。对于检测，首先，我们提出了鲁棒离群比（ROR）来衡量每个像素的脉冲特性，然后根据 ROR 值将所有像素分为四个聚类。其次，根据每个聚类中对中值的绝对偏差，使用不同的决策规则来检测脉冲噪声。为了使检测结果更加准确和稳健，采用了从粗到细的策略和迭代框架。此外，检测过程包括粗检测和细检测两个阶段。对于滤波，通过引入参考图像将 NL-means 扩展到脉冲噪声。然后，通过将新的检测机制与 NL-means（ROR-NLM）相结合，提出了一种通用的去噪框架。最后，大量的仿真结果表明，所提出的噪声检测器优于大多数现有的检测器，而 ROR-NLM 针对不同噪声模型产生了出色的结果，并优于大多数现有的滤波器。与大多数其他脉冲噪声滤波器不同，所提出的 ROR-NLM 还可以通过有效地去除脉冲/高斯混合噪声来实现高峰值信噪比和良好的图像质量。
