Ontology-based Genetic Fuzzy Filter for image Processing

This paper proposes an ontology-based genetic fuzzy filter (OGFF), including a noise ontology, a fuzzy filtering process, and an intelligent learning process, to remove impulse noise from highly corrupted images. A noise ontology referred by the fuzzy filtering process is utilized to perform the task of noise removal. Then, using orthogonal arrays and factor analysis, a genetic algorithm is applied to the intelligent learning process. Finally, the parameters of the noise ontology are adjusted via the intelligent learning process to increase the performance of image filtering. Experimental results show that the proposed approach can achieve better performance than the state-of-the-art filters based on the criteria of mean-absolute-error (MAE), mean-square-error (MSE), and peak-signal-to-noise-ratio (PSNR). Additionally, on the subjective evaluation of those filtered images, the proposed approach can also generate a higher quality of global restorations