Quantum Image Edge Detection Schemes Based On Hadamard Gate And Double Chains Quantum Genetic Algorithm

As an important part of digital image processing,edge detection technology has been developed rapidly in recent years.The technology can be applied in tumor edge detection,fingerprint unlocking,license plate recognition,satellite positioning,iris recognition,face detection and trademark image retrieval.However,with the continuous innovation of the technology,people are no longer limited to extract the rough edges of digital images,but seek more finer edges and faster detection schemes.Two quantum edge detection algorithms are designed based on the Hadamard gate,the double chains quantum genetic algorithm(DCQGA)and the quantum smallest univalue segment assimilating nucleus(SUSAN)edge detection operator.The main research works are as follows:To reduce the complexity of the algorithm,a quantum image edge detection algorithm based on Hadamard gate is proposed.After a series of steps such as novel enhanced quantum representation(NEQR),Hadamard transform,probabilistic amplitude displacement and frequency discrimination measurement,the quantum edge image is obtained.The algorithm can find the edge gradient and output the quantum edge image with the lowest algorithm complexity.The performances of various quantum edge detection algorithms are analyzed with multiple evaluation indexes such as sensitivity,specificity,peak signal to noise ratio,and structural similarity.It is found that the image edges tested by the algorithm are more continuous and less distorted.The feasibility and robustness of the algorithm are verified by simulation experiments.A quantum SUSAN edge detection method based on double chains quantum genetic algorithm is proposed to reduce the complexity and improve the accuracy of detection.To reduce transmission distortion and running time,the cyclic X and the cyclic Y shift transforms are introduced in the algorithm.The gradient values between pixels are calculated by the scale-dependent gradient module,and then the obtained gradient values are transmitted into the quantum SUSAN classifier.The SUSAN edge detection classifier classifies edge points and non-edge points according to the gradient value.Finally,the inner texture edges are suppressed with the surround suppression method,and the quantum edge image is obtained.The algorithm has lower complexity than many quantum edge detection algorithms,and the extracted edges have more details and are more continuous.Simulation experiments show that the proposed algorithm is superior to the classical edge detection algorithms and several quantum edge detection algorithms in terms of feasibility and accuracy.