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Improving the quality of medical images in shearlet domain

机译:改善小波域医学图像的质量

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In clinical diagnostics, the diagnostic approach based on images obtained from equipment of which medical machine (diagnostic imaging) plays an important role. However, most of medical images have blur combined with noise. There are many reasons to create blur combined with noise in medical images such as the environment, capture device, technician's skills, etc. This problem will affect the process diagnose. In this paper, we proposed a new method to improve the quality of medical images. The proposed method uses cycle spinning combined with Kernels set in shearlet domain. Our algorithm removes the noise and blur details in shearlet domain, and must not the value of point-spread function (PSF). The proposed algorithm not only significantly improves the edge accuracy, but also reduces the loss of information in medical image.
机译:在临床诊断中,基于由医疗机器(诊断成像)的设备获得的图像的诊断方法起着重要作用。但是,大多数医学图像都有模糊与噪音结合。在诸如环境,捕获设备,技术人员的技能等中,创建模糊与噪声相结合的原因很多。此问题将影响过程诊断。在本文中,我们提出了一种提高医学图像质量的新方法。该方法使用循环旋转与在Shearlet结构域中设置的内核联合。我们的算法在Shearlet域中消除了噪声和模糊细节,并且不得点扩展功能(PSF)的值。所提出的算法不仅显着提高了边缘精度,而且还减少了医学图像中的信息丢失。

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