中文摘要：

目的 医学超声图像常常受到斑点噪声的污染而导致质量降低,影响后续诊疗.为了解决医学超声图像在滤波去斑的同时保持图像边缘细节和结构特征的问题,借鉴量子力学的基础理论,提出一种量子衍生偏微分方程（PDE）医学超声图像去斑方法.方法 针对传统P-M方程各向异性扩散的自适应去斑能力有限的问题,引入量子理论改进扩散系数增强去斑算法的自适应能力.同时构造出各向异性扩散模型,提出一种量子衍生的偏微分方程医学超声图像去斑方法.结果 通过对模拟斑点噪声污染的图像和真实医学超声图像实验,比较信噪比（SNR）、边缘保持度、结构相似度（SSIM）等客观评价指标,本文方法较其他图像去斑方法更能有效去除斑点噪声,同时又能较好地保持图像边缘细节与结构特征.结论 本文方法能够有效地解决医学超声图像去斑中保持图像细节特征的问题,同时,量子理论的引入也为后续医学超声图像的研究提供了新思路.

英文摘要：

Objective Uhrasonography is one of the most important modalities of medical imaging system, and medical ultra- sound images play a significant role in medical imaging techniques. However, medical ultrasound images are always con- taminated by a noise called ＂speck noise＂, which has a visual effect similar to speck, instead of the point-like Gaussian white noise. Speck noise seriously degrades the quality of medical ultrasound images. Thus, in a contaminated medical ul- trasound image, the observer has difficulty discriminatingthe fine details and structural features, hindering the application of ultrasound images in clinical diagnosis and treatment. In this paper, the quantum-inspired diffusion coefficient is introduced to discuss the challenge of despeckling while preserving the edge detail and structural features of ultrasound images. Method The proposed method improves the diffusion coefficient in traditional P-M equations based on the denoising method by some foundational knowledge in quantum theory. Anisotropic diffusion model is built on the basis of the traditional P-M equations. The proposed quantum-inspired diffusion coefficient changes over the gradient direction to take advantage of the better directional selectivity of wavelet coefficients. The optimization of this coefficient can be strengthened by the improved anisotropic diffusion model. Thus, a novel quantum-inspired partial differential equation based on medical ultrasound image despeckling method is proposed. Result Experiments are conducted on both images with simulation speck noise and real medical ultrasound images to show the performance of the proposed method in comparison with other classic despeckling methods. Among all compared methods, the proposed method obtains the best objective evaluation, such as signal-to-noise ratio, edge preserve measurement, structural similarity index measurement, and equivalent number of looks. Experimental results of both images with simulation speck noise and real medical ultrasound images can demonstra