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Machine learning and directional switching median-based filter for highly corrupted images

机译：机器学习和基于方向切换的基于中值的滤波器用于高度损坏的图像

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摘要

In this paper, two-stage machine learning-based noise detection scheme has been proposed for identification of salt-and- pepper impulse noise which gives excellent detection results for highly corrupted images. In the first stage, a window of size 3 × 3 is taken from image and some other features of this window are used as input to neural network. This scheme has distinction of having very low missed detection (MD) and false positives rates. In the second stage, decision tree-based algorithm (J48) is applied on some well-known statistical parameters to generate rules for noise detection. These noise detection methods give promising results for identification of noise from highly corrupted images. A modified version of switching median filter (directional weighted switching median filter) is proposed for noise removal. Performance of noise detector is measured using MD and false alarm FA. Filtering results are compared with state-of-the-art noise removal techniques in terms of peak signal-to-noise ratio and structural similarity index measure. Extensive experiments are performed to show that the proposed technique gives better results than state-of-the-art noise detection and filtering methods.
机译：本文提出了一种基于两步机器学习的噪声检测方案，用于识别椒盐冲激脉冲噪声，该方法对于高度损坏的图像提供了出色的检测结果。在第一阶段，从图像中获取一个大小为3×3的窗口，并将该窗口的其他一些特征用作神经网络的输入。该方案的区别在于具有非常低的漏检（MD）和误报率。在第二阶段，将基于决策树的算法（J48）应用于一些众所周知的统计参数，以生成用于噪声检测的规则。这些噪声检测方法为从高度损坏的图像中识别噪声提供了有希望的结果。提出了开关中值滤波器（方向加权开关中值滤波器）的改进版本以消除噪声。噪声检测器的性能使用MD和错误警报FA进行测量。在峰值信噪比和结构相似性指标衡量方面，将过滤结果与最新的噪声消除技术进行了比较。进行了广泛的实验，表明所提出的技术比最新的噪声检测和滤波方法能提供更好的结果。

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