An image enhancement method based on improved teaching-learning-based optimization algorithm

To improve image quality and render the enhanced image more suitable for subsequent image processing, an image enhancement method based on improved teaching-learning-based optimization(TLBO) algorithm is presented. First, combining local information with global information, the original image is converted into the enhanced image. Subsequently, an image enhancement optimization model and an evaluation function including edge intensity, edge pixels, and entropy were established. Second, the TLBO algorithm was modified in two aspects: to raise the global search capability and convergence precision the teaching factor was adaptively adjusted for coordinating the diversity and convergence of the population, and an optimal individual guidance mechanism was produced to speed up the convergence. The suggested TLBO was first applied to optimize the image enhancement optimization model. Experiment results show that compared with other methods, the proposed method has better visual effects and image quality. © 2016, Editorial Department of Journal of HEU. All right reserved.