Turbulent-PSO-Based Fuzzy Image Filter With No-Reference Measures for High-Density Impulse Noise

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.