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A Nonlinear Coupled Diffusion System for Image Despeckling and Application to Ultrasound Images

机译:用于图像去斑的非线性耦合扩散系统及其在超声图像中的应用

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Despite extensive availability of filters for denoising, speckle noise suppression remains a challenging task. Speckle noise also hinders tasks such as efficient extraction of features, recognition, analysis, detection of edges, etc. Therefore, in this paper, motivated by the impressive performance of time delay regularization in additive noise removal, we develop a class of nonlinear diffusion-based coupled partial differential equation models for multiplicative noise removal. This denoising framework considers separate partial differential equations to handle diffusion function as well as fidelity term. By using a maximum a posteriori regularization approach, we can derive an energy functional and study the associated evolution problem which corresponds to the denoised image we want to recover. We then evaluate the effectiveness of our model with several standard test images and real ultrasound images. Qualitative and quantitative studies confirm that the proposed model is robust in comparison with state-of-the-art approaches. The denoised images have appealing visual characteristics with different levels of noise and textures while preserving the important details of the original image.
机译:尽管有大量用于降噪的滤波器,但散斑噪声抑制仍然是一项艰巨的任务。斑点噪声还阻碍了诸如特征的有效提取,识别,分析,边缘检测等任务。因此,在本文中,受时间延迟正则化在去除附加噪声中令人印象深刻的性能启发,我们开发了一类非线性扩散-基于耦合的偏微分方程模型进行乘除噪。该降噪框架考虑单独的偏微分方程来处理扩散函数以及保真度项。通过使用最大后验正则化方法,我们可以推导能量函数并研究与我们要恢复的降噪图像相对应的关联进化问题。然后,我们使用几个标准测试图像和真实的超声图像评估模型的有效性。定性和定量研究证实,与最新方法相比,该模型具有较强的鲁棒性。去噪的图像具有吸引人的视觉特征,具有不同级别的噪声和纹理,同时保留了原始图像的重要细节。

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