Research On Image Processing Methods Based On Quantum Mechanics

Quantum mechanics is one of the most important scientific achievements of physics in the twentieth century. Microscopic world is not only dominated by the laws of quantum mechanics, but also the macroscopic world is also done. As an objective physical entity in nature, the Image signal is also affected by the physical constraints of quantum mechanics. With the principle, the basic concepts of quantum mechanics and the advantages of quantum properties, the novel methods of image processing are proposed for the solution of some specific problems in the classical computer, which don't depend on the quantum level of physical equipment. It promotes the realization of the combination and mutual penetration between quantum mechanics and image processing technology. Not only the better image processing results are achieved, but also a new theoretical tool is introduced into the image processing theory.From practical applications of image processing, three key image processing techniques are studied with the basic concept and principle of quantum mechanics in this thesis which focus on the image despeckling, image enhancement and image segmentation. The main work of this thesis is summarized as follows.Firstly, through analyzing and comparing various image despeckling methods, two quantum-inspired despeckling methods for medical ultrasound images are proposed by combining the dual-tree complex wavelet transform (DTCWT) with the basic theory of quantum mechanics. In the two proposed methods, two improved signal models with an adjustable parameter are built up for the log-transformed images wavelet coefficients firstly. Both the improved signal models have much better adaptability than the traditional signal models which can suit different probability distribution signals and the fitting procedure of adjustable parameter is simple and effective. And then, considering the inter-scale dependency of coefficients, the quantum-inspired probability of signal and noise is firstly introduced based on the normalized products of the coefficients and their parents. Finally, using the Bayesian estimation theory, two image despeckling methods are proposed, where one is based on a quantum-inspired shrinkage factor and the other is based on quantum-inspired threshold. By adopting different local adaptive quantum-inspired parameters, both methods can notably reduce speckle noise and preserve image details effectively, which have low computational complexity, strong adaptability and robustness. In addtion, both methods have universal applicability to some degree, which can effectively suppress speckle noise not only for medical ultrasound images but also Synthetic Aperture Radar (SAR) images, which have good generalization and application.Secondly, two different mathematics expressions of pixel quantum bit are given first according to the basic principle of quantum mechanics. Then, aiming at the characteristics of medical images and combining with gray correlative characteristics of pixels in 3x3 neighborhoods, an image enhancement operator is proposed based on quantum probability statistics. In order to optimize the effect of image enhancement, the gray threshold parameter of the operator is adaptively chosen based on the sub-sampling image entropy. The proposed image enhancement method considers both global and local image information and can improve images quality effectively. In addtion, it has low computational complexity and universal applicability to some degree, which can not only enhance medical images effectively, but also improve vision effect of nonmedical images.Finally, an adaptive membership function is defined and a method of multi-threshold image segmentation is proposed based on the adaptive maximum fuzzy entropy. In order to improve searching efficiency of multi-threshold, quantum genetic algorithm is applied to image segmentation and some improvements of existed quantum genetic algorithm are made which can not only increase accuracy and stablility for the results of multi-threshold image segmentation but also improve the real-time dealing ability. In this thesis, a method of multi-threshold image segmentation is proposed based on an improved quantum genetic algorithm, which combines with the evaluation function of adaptive maximum fuzzy entropy. Experimental results demonstrated that the proposed method had a more stable performance of solution and can achieve much better image segmentation effect.