IEEE Trans Cybern. 2013 Feb;43(1):296-307. doi: 10.1109/TSMCB.2012.2205678. Epub 2012 Jul 20.
Digital images are often corrupted by impulsive noise during data acquisition, transmission, and processing. This paper presents a turbulent particle swarm optimization (PSO) (TPSO)-based fuzzy filtering (or TPFF for short) approach to remove impulse noise from highly corrupted images. The proposed fuzzy filter contains a parallel fuzzy inference mechanism, a fuzzy mean process, and a fuzzy composition process. To a certain extent, the TPFF is an improved and online version of those genetic-based algorithms which had attracted a number of works during the past years. As the PSO is renowned for its ability of achieving success rate and solution quality, the superiority of the TPFF is almost for sure. In particular, by using a no-reference Q metric, the TPSO learning is sufficient to optimize the parameters necessitated by the TPFF. Therefore, the proposed fuzzy filter can cope with practical situations where the assumption of the existence of the "ground-truth" reference does not hold. The experimental results confirm that the TPFF attains an excellent quality of restored images in terms of peak signal-to-noise ratio, mean square error, and mean absolute error even when the noise rate is above 0.5 and without the aid of noise-free images.
数字图像在数据采集、传输和处理过程中经常会受到脉冲噪声的干扰。本文提出了一种基于紊流粒子群优化（TPSO）的模糊滤波（TPFF）方法，用于去除高度受污染图像中的脉冲噪声。所提出的模糊滤波器包含一个并行模糊推理机制、一个模糊均值处理和一个模糊合成处理。在某种程度上，TPFF 是对那些基于遗传算法的改进和在线版本，这些算法在过去几年中吸引了许多研究工作。由于 PSO 以其成功率和解决方案质量的能力而闻名，因此 TPFF 的优越性几乎是确定的。特别是，通过使用无参考 Q 度量，TPSO 学习足以优化 TPFF 所需的参数。因此，所提出的模糊滤波器可以应对“真实参考”假设不成立的实际情况。实验结果证实，即使在噪声率高于 0.5 且没有无噪声图像的情况下，TPFF 也能在峰值信噪比、均方误差和平均绝对误差方面获得出色的图像恢复质量。
