Adaptive DE based on chaotic sequences and random adjustment for image Contrast Enhancement

Abstract

Differential Evolution (DE) is one of the powerful optimization methods. Performance of this algorithm is significantly relying on its parameter setting. These parameters are usually constant during the entire search process. However to set them accurately is not easy and totally depends on the problem characteristic. To address this challenge, a number of methods have been proposed to automatically fine-tune the parameters, according to feature of the problem. In this paper we evaluated two variations of adaptive DE for application of optimal image Contrast Enhancement. The first method was DE using chaotic sequences and the second was DE based on random adjustment of the parameters. The objective of both variations in this application is to increase the fitness criterion with the aim of enhance the contrast and details of the image. The results are compared with classical DE by four testing images, i.e. Cameraman, Lena, Boat, and Rice. The simulation results show that, applications of these variations adaptive DE in image contrast enhancement are potential approach to increase the performance of classical DE.