Recently 3D image processing has interested researchers in both theoretical and applied fields and thus has constituted a challenging subject. Theoretically, this needs suitable functional bases that are easy to implement by the next. It holds that Clifford wavelets are main tools to achieve this necessity. In the present paper we intend to develop some new classes of Clifford wavelet functions. Some classes of new monogenic polynomials are developed firstly from monogenic extensions of 2-parameters Clifford weights. Such classes englobe the well known Jacobi, Gegenbauer and Hermite ones. The constructed polynomials are next applied to develop new Clifford wavelets. Reconstruction and Fourier-Plancherel formulae have been proved. Finally, computational examples are developed provided with graphical illustrations of the Clifford mother wavelets in some cases. Some graphical illustrations of the constructed wavelets have been provided and finally concrete applications in biofields have been developed dealing with EEG/ECG and Brain image processing.

最近，3D图像处理引起了理论和应用领域的研究人员的兴趣，因此构成了一个具有挑战性的课题。从理论上讲，这需要合适的功能基础，这些功能基础接下来很容易实现。它认为Clifford小波是实现此必要性的主要工具。在本文中，我们打算开发一些新的Clifford小波函数类。首先从2参数Clifford权重的单基因扩展发展出一些新的单基因多项式。这样的阶级笼罩着著名的雅可比，杰根鲍尔和埃尔米特的阶级。接下来将构造的多项式应用于开发新的Clifford小波。重构和傅立叶-普歇尔公式已被证明。最后，在某些情况下，提供了一些计算示例，并提供了Clifford母波的图解说明。提供了一些构造的小波的图形说明，最后开发了在生物领域中处理脑电图/心电图和脑图像处理的具体应用。