基于2D-VMD与CC结合的模态重构算法

为提高重构图像的质量，针对二维变分模态分解( 2D-VMD: Two Dimensional Variational Mode Decomposition) 算法需确定分解尺度K 值和有效固有模态分量的问题，提出了将2D-VMD 结合相关系数( CC: Correlation Coefficient) 的联合算法，并用于图像重构。该方法首先利用2D-VMD 将图像信号分解为不同中心频率的子模态，然后计算分解后的固有模态函数( IMF: Intrinsic Mode Function) 与原始图像函数的CC 值，根据CC 准则确定有效的固有模态分量，最后利用有效的固有模态分量进行重构，实现了图像去噪。仿真结果表明，2D-VMD 和CC 结合可以准确得到分解尺度K 和有效的子模态，具有很高的图像重构精度，提高了图像质量。

Reconstruction Algorithm Based on 2D-VMD and CC

To improve the quality of reconstructed images，it is necessary to determine the decomposition scale K value and the effective intrinsic mode function in 2D-VMD ( Two Dimensional Variational Mode Decomposition) algorithm，a new algorithm combined 2D-VMD and CC ( Correlation Coefficient) is proposed and used to reconstruct image． Firstly，the image signal is decomposed into sub-modes of different central frequencies by 2D-VMD． Then，the CC value of the decomposed Intrinsic Mode Function ( IMF) and the original image function is calculated to determine the effective Intrinsic Mode component according to the CC criterion．Finally，image denoising is achieved by reconstruct with effective intrinsic modal component． Simulation results show that the decomposition scale K and the effective sub-modes can be obtained through combined 2D-VMD and CC，which has high image reconstruction accuracy and improved image quality．