Nature Inspired Algorithms multi-objective histogram equalization for Grey image enhancement

Abstract

Nature is a very rich source of inspiration. Many algorithms have inspired from nature and source of algorithms inspiration development are diverse with different quality.  Nature–inspired optimization techniques play an essential role in the field of image processing. It reduces the noise and blurring of images with improves the image enhancement, image segmentation, image pattern recognition. The Image enhancement is a process to make image ready for further uses in certain applications. The image quality is individually related with its contrast by rising the contrast, further disfigurements can be produced. In this paper covers current equalization enhancement technique some nature inspired algorithm for medical images. In addition, proposed an image enhancement method built by using two natures inspired algorithms Particle Swarm Optimization (PSO) and Bat Optimization Algorithms (BOA) combined to produce better enhancement. Here an objective criterion for measuring image enhancement is used which considers the Discrete Entropy (DE), the Structural Similarity Index Matrix (SSIM) and Executing Time (ET). The results showed the Bat Algorithm has produced a batter enhanced images when comparing with Particle Swarm Optimization images and the existing histogram-based equalization methods. The final results showed proposed image enhancement method can not only improve the contrast of the image, but also preserve the details of the image, which has a good visual effect.