基于二维变分模态分解与自适应分数阶积分的图像去噪方法

Image Denoising Method Based on Two-dimensional Variational Modal Decomposition and Adaptive Fractional Integration

针对噪声对图像分辨率的影响,提出了一种基于二维变分模态分解(two-dimensional variational modal decomposition,2D-VMD)与分数阶积分的去噪算法。首先通过2D-VMD将图像信号分解为若干个不同中心频率的本征模态分量(intrinsic modal components,IMF),筛选有效的低频IMF分量,根据图像信息差异设定阈值,进行分数阶积分自适应选取,对每个有效的分量图进行卷积运算,根据积分阶次用方向掩模去噪算子滤除噪声,最终完成图像去噪。实验结果表明,客观评价参数值均得到提高,该方法在滤除噪声的同时也能够较好地保持图像的轮廓或纹理等细节特征。

Aiming at the effect of noise on image resolution,a denoising algorithm based on two-dimensional variational modal decomposition(2D-VMD)and fractional integration was proposed.First,the image signal was decomposed into a number of intrinsic modal components(IMF)with different center frequencies by 2D-VMD,effective low-frequency IMF components are screened,thresholds were set according to the difference in image information,and fractional integral was adaptively selected.Convolution operation was performed on an effective component image,and the noise was filtered by the directional mask denoising operator according to the order of integration,and the image denoising was finally completed.The experimental results show that the objective evaluation parameter values have been improved,and this method can better maintain the details of the image such as contours or textures while filtering out the noise.