Agent-Based Image Contrast Enhancement Algorithm

Abstract

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

Preventing Over-Enhancement Using Modified ICSO Algorithm

“…The mentioned complex optimization problems can be effectively solved by Nature-Inspired Optimization Algorithms (NIOAs) using single or multiple objective functions [17] and different learning methods. NIOAs such as Artificial Bee Colony (ABC) [18], [19], Cuckoo Search (CS) [20], [21], Particle Swarm Optimization (PSO) [22], Firefly Algorithm (FA) [23], Wind-Driven Optimization (WDO) [24], Chicken Swarm Optimization (CSO) [25], and Moth Swarm Algorithm [26], [27] are effectively used in image processing. NIOAs have been also employed in variant applications such as image segmentation, classification, and compression [28], [29], [30].…”