Problems such as low contrast, noise, and edge blur are often encountered in color infrared images produced by infrared cameras. To solve these problems, we propose a new image enhancement algorithm based on the gravitational force and lateral inhibition network. First, information on total gravitational force for each dimension of the color infrared image was obtained. These two-dimensional three gray level images obtained using three-dimensional color properties help to define noise, edge and region within each dimension. Secondly, these three gray level images were subjected to a dual threshold value. A mean filter was used to reduce noise, while the lateral inhibition network was used for resolution and edge detection, and the regional gravity factor was used for contrast control. Finally, each dimension was combined again and a color enhanced image was produced. This study sets out to develop a method of enhancement images for infrared image analysis in cooling systems. The images...
Abstract Gravitational search algorithm (GSA) is based on the feature of reciprocal acceleration tendency of objects with masses. The total force, which is formed as an influence of other agents, is an important variable in the calculation of agent velocity. It has been determined that the total force and, thus, the velocity of the agents that are located far away, is low due to the distance. In this case, they continue their search in bad areas, as their velocity is low, which means a decrease in their contribution to optimization result. In this paper, a new operator called “escape velocity” has been proposed which is inspired by the real nature of GSA. It has been suggested that adding the escape velocity negatively will enable the agents that remain far away or outside of group behavior to be included in the group or to be increased in velocity. Thus, the study of perfecting the herd or group approach within the search scope has been carried out. To evaluate the perf...