Infrared image enhancement model based on gravitational force and lateral inhibition networks

Abstract

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 used in the study are made up of a compressor, a condenser, and an evaporator belonging to the cooling system. The implementation of our method is simple and easy to understand and yields more accurate results. The experimental results show that the proposed method can eliminate noise, blur, and low contrast, and can also improve the details of infrared images better than other methods.