基于快速自适应的二维经验模态分解的图像去噪算法

Image Denoising Algorithm Based on Fast and Adaptive Bidimensional Empirical Mode Decomposition

为了能够对图像进行自适应的分解,并准确刻画分解系数的分布状态,提出了一种新的基于快速自适应二维经验模态分解的图像去噪算法。该算法首先对图像进行快速自适应二维经验模态分解,通过确定分解后以噪声主导的子带的个数,进一步利用正态逆高斯模型对以噪声主导的子带系数分布进行建模;然后使用贝叶斯最大后验概率估计理论从模型导出相应的阈值;最后采用最优线性插值阈值函数算法完成去噪。仿真结果表明,对于添加不同标准差大小高斯白噪声的测试图像,所提算法在峰值信噪比上相比sym4小波去噪、双变量阈值去噪、邻近算子的全变分算法和重叠组稀疏的全变分算法分别平均提高了4.36 dB,0.85 dB,0.78 dB和0.48 dB,结构相似性指数也有不同程度的提高,有效地保留了更多的图像细节。实验结果证明,所提算法在视觉性能和评价指标方面均优于对比算法。

In order to adaptively decompose the image and accurately describe the distribution state of the decomposition coefficients,a new image denoising algorithm based on fast and adaptive bidimensional empirical mode decomposition algorithm was proposed.Firstly,the algorithm performs fast and adaptive bidimensional empirical mode decomposition on the image.By determining the number of noise-dominated subband after decomposition,the noise-dominated subband coefficient distribution is further modeled by the normal inverse Gaussian model.Then the Bayesian maximum posteriori probability estimation theory is used to derive the corresponding threshold from the model.Finally,the optimal linear interpolation threshold function algorithm is used to complete the denoising.The simulation results show that for adding Gaussian white noise images of different standard deviation,the average signal-to-noise ratio is improved by 4.36 dB,0.85 dB,0.78 dB and 0.48 dB,respectively,compared with sym4 wavelet denoising,bivariate threshold denoising,pro-ximity algorithms for total variation,and overlapping group sparse total variation algorithm.Structural similarity index is also improved with different degrees,which shows it can effectively preserve more image details.The experimental results show that the proposed algorithm is superior to the comparison algorithms in terms of visual performance and evaluation index.