Speckle noise is a kind of ubiquitous noise in medical image, which will damage the texture structure of image and affect the analysis of image structure by doctors. Therefore, we propose an image denoising model based on fuzzy logic, which can eliminate speckle noise in the image well, improve the recognition of the image, and facilitate the acquisition of image information by doctors. The main work arrangement of the algorithm model is to design a membership function that can traverse the noise image and preprocess the noise image to make the image smooth. Then, a mask template of 5 × 5 is designed by the definition of g‐l calculus, and there is mainly an unknown parameter in this template. We design the functional relation between this parameter and the image gradient, which makes the model algorithm adaptive. Finally, the convolution operation is performed between the template and the smooth image. By comparison with the existing mainstream models, the overall denoising effect of this model is better than other models, and the relevant numerical indexes are better than other models. This model is an extension of the denoising model of fuzzy theory, which is beneficial to the future research and development.

中文翻译：

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基于模糊逻辑的斑点噪声图像自适应去噪

散斑噪声是医学图像中普遍存在的一种噪声，会破坏图像的纹理结构，影响医生对图像结构的分析。因此，我们提出一种基于模糊逻辑的图像去噪模型，可以很好地消除图像中的散斑噪声，提高图像的识别度，方便医生获取图像信息。算法模型的主要工作安排是设计一个隶属函数，可以遍历噪声图像并对噪声图像进行预处理，使图像平滑。然后根据g-l演算的定义设计了一个5×5的mask模板，这个模板中主要有一个未知参数。我们设计了这个参数和图像梯度之间的函数关系，使模型算法具有自适应性。最后，在模板和平滑图像之间进行卷积操作。与现有主流模型相比，该模型的整体去噪效果优于其他模型，相关数值指标优于其他模型。该模型是模糊理论去噪模型的扩展，有利于未来的研究和发展。