Agent-Based Image Contrast Enhancement Algorithm

One crucial step in several image processing and computer vision applications is Image Contrast Enhancement (ICE), whose main objective to improve the quality of information contained processed images. Most proposed schemes attack problem by redistributing pixel intensities a histogram, leading undesirable effects such as noise amplification, over-saturation, lousy human perception. On other hand, Agent-Based Models (ABM) are computational models that allow describing behavior interactions autonomous agents when they operate cooperatively. These follow behavioral rules rather than mathematical formulations. This mechanism allows implementation complex patterns through their interactions. paper proposes two-step method where pixels considered permit enhance significatively contrast. In our approach, among characterized differences intensity values or agents. first step, present enough high modified increase even more differences. second maintain very small difference altered assume homogeneous value. The approach has been tested considering different public datasets commonly used literature. Its results also compared with those produced well-known ICE techniques. Evaluation experimental demonstrates highlights important details taking lower execution time.