A GENETIC ALGORITHM FOR LEARNING IMAGE BLUR AND SHARPEN FILTERS

Abstract

In this paper, N-Queens problem was chosen to compare GA with PSO performance. GA with its own simple operators is stable in its performance under different search space sizes, while the PSO performs well in small search space size and its capabilities when space size becomes larger. This paper presents an approach for learning traditional image filters (blurring and sharpening). The concept of learning is based on the mechanism of Genetic algorithm (GA). By GA, filters applied on one source image can be learned and then used to process automatically another target image. By this way, blurring and sharpening can be implicitly deduced and applied without requiring to mathematically defining (i.e. explicitly) them. The proposed approach is simple and can provide good results; however, applying the filter directly is much more efficient.