Color Image Enhancement: A Metaheuristic Chimp Optimization Algorithm

The traditional incomplete Beta function has low parameter selection efficiency in image enhancement, and the adjustable range of parameters is small when stretching the area with low or high gray level, and the image enhancement with stretching at both ends and compression in the middle is almost ineffective. Therefore, an image enhancement method based on bilateral gamma correction and incomplete Beta function fusion is proposed. In this method, meta-heuristic chimp optimization algorithm is used to adaptively select the parameter values of the incomplete Beta function, bilateral gamma correction is used to further enhance the image contrast. In order to verify the effectiveness and feasibility of this method, 12 color images with contrast distortion and 15 contrast methods were selected. The experimental results show that our proposed method has good enhancement effect in avoiding over enhancement and mixing complex images with multiple attributes, and retains more image details. The color images enhancement effects are significantly better than other contrasted popular image enhancement methods.