Morphological image enhancement procedure design by using genetic programming

In this paper, we propose a genetic programming algorithm to design the morphological image enhancement procedure. Given a group of morphological operations and logical operations as function set, this algorithm evolves to produce a rational procedure which can enhance the input images. A novel mechanism which combines the ground truth method and feature significance is brought forward to evaluate the performance of images enhanced by generated procedures. In each generation, the best fitted individuals are selected on the basis of fitness values, and some individuals participate in crossover or mutation with a probability. After each generation, this algorithm outputs the best individual. Seven morphological operations and five logical operations are used in this algorithm. Furthermore, the structuring elements of morphological operations are randomly generated and varied in the whole pattern space. These methods promote the expressive ability of generated procedures. Examined by the binary image feature extraction, the procedure generated by this algorithm is more accurate and intelligible than previous work. In the task of gray scale image enhancement, the generated procedure is applied to infrared finger vein images to enhance the region of interest. More accurate features are extracted and the accuracy of authentication is promoted.

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