Improved Quantum Artificial Bee Colony Algorithm Based On Bloch Spherical Surface And Its Application In Image Threshold Segmentation

Image segmentation is a key step in image processing, threshold method is a common method in image segmentation, how to get a reasonable segmentation threshold is the key problem of threshold segmentation. There is still room for improvement of the existing artificial bee colony algorithm and its improved algorithm in image segmentation, qubit Bloch spherical coordinate is used to improve artificial bee colony algorithm in this paper, an improved quantum bee colony(Bloch Quantum Artificial Bee Colony, BQABC) algorithm based on Bloch spherical surface is proposed, the two dimensional linear cross entropy of the image is used as the fitness function, threshold segmentation is researched, the contributions of this paper are as follows:First of all, the defects and deficiencies of the basic artificial bee colony algorithm are analyzed, Bloch spherical coordinate was used for encoding the initial nectar, the number of global optimal solutions is extended, the idea of quantum entanglement and the global optimal guidance is introduced to improve the update formula of employed foragers based on the nectar update mechanism of bee colony algorithm, the cosine function is used to form the global optimal item information into the update formula for each iteration, BQABC algorithm is proposed;Secondly, the theory of random process and Markov chain is used to prove that the improved algorithm is convergent with probability 1, this improved algorithm has the ability to find the optimal threshold in image segmentation that is explained in theory, theoretical foundation is laid in instance verification.Thirdly, the effect of algorithm parameters on function optimization is analyzed, the BQABC algorithm and Artificial Bee Colony Artificial(ABC) algorithm are used to optimize the function of the test function which has typical features in the function library, the algorithm has a good ability to obtain the optimal value is showed through simulation results of different type functions;Finally, the time complexity of BQABC algorithm is analyzed, the two dimensional linear cross entropy is used as the fitness function, the OTSU algorithm, ABC algorithm and BQABC algorithm are applied to the threshold segmentation of the standard image, the network image and the non standard image with noise, response curve and performance index are compared to analyzed, the BQABC algorithm can get the segmentation threshold accurately and quickly and has a good image segmentation effect which are verified in theactual data; at the same time, in order to test the denoising effect, the peak signal to noise ratio of the denoising process is also obtained, the threshold segmentation results of the BQABC algorithm for the above image is indirectly reflected.