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首页> 外文期刊>Information Technology Journal >A Novel Adaptive Regularized Possibilistic Linear Models Based Median Filter ARBMF for Image Noise Suppression
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A Novel Adaptive Regularized Possibilistic Linear Models Based Median Filter ARBMF for Image Noise Suppression

机译：基于中值滤波器ARBMF的新型自适应正则化可能性线性模型

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摘要

Based on possibility concepts, various Possibilistic Linear Models (PLMs) have been proposed and their pivotal role in fuzzy modeling and the associated applications have been established. The Regularized Possibilistic Linear Model (RPLM) is a regularized version of PLM which can enhance the generalization capability of PLM. In present study, a novel Adaptive RPLMs Based Median Filter (ARBMF) is proposed for improving the performance of median-based filters, preserving image details while effectively suppressing impulsive noises The proposed filter achieves its effect through the linear combinations of the weighted output of the median filter and the related weighted input signal and the weights are set based on regularized possibilistic linear models concerning the states of the input signal sequence. Experimental results for benchmark images demonstrate that the proposed filter outperforms a number of extensively-used median-based filters Moreover, the proposed filter also provides excellent robustness with respect to various percentages of impulse noise in our testing examples.
机译：基于可能性概念，提出了各种可能的线性模型（PLM），并建立了它们在模糊建模中的关键作用及其相关应用。正则化可能性线性模型（RPLM）是PLM的正则化版本，可以增强PLM的泛化能力。在本研究中，提出了一种新颖的基于自适应RPLM的中值滤波器（ARBMF），以提高基于中值的滤波器的性能，在保留图像细节的同时有效地抑制脉冲噪声。所提出的滤波器通过线性加权输出的加权组合来达到其效果。基于关于输入信号序列的状态的正规化的可能线性模型来设置中值滤波器以及相关的加权输入信号和权重。基准图像的实验结果表明，所提出的滤波器优于许多广泛使用的基于中值的滤波器。此外，在我们的测试示例中，所提出的滤波器还针对各种百分比的脉冲噪声提供了出色的鲁棒性。

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