A visual consistent adaptive image thresholding method

Abstract

Image thresholding method is an important preprocessing step in many applications of image processing. In this paper, we present a visual consistent adaptive image thresholding method, by combining the thresholding process with the characteristics of human visual system. In the proposed algorithm, we first roughly classify the image pixels into two categories. Then two sub-images are constructed to retain the essential information of the original image. For each sub-image, a corresponding global optimal threshold is calculated by optimizing an objective function. Finally, a visual consistent binary image is produced by combining the results from the previous steps with the information of pixels in each category. The proposed method has been tested on some different images, and the results are also compared with a number of known algorithms in the literature. The experimental results demonstrate that the overall visual qualities of our method are more suitable for human visual perception.

Keywords:

• Thresholding method
• Particle swarm optimization (PSO)
• Fitness function
• Threshold optimization
• Visual quality