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首页> 外文会议>International conference on mechatronics technology >Wavelet Threshold Denoising of Infrared Image Based on Bidimensional Empirical Mode Decomposition
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Wavelet Threshold Denoising of Infrared Image Based on Bidimensional Empirical Mode Decomposition

机译：基于二维经验模态分解的红外图像小波阈值去噪

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摘要

A method of infrared image denoising was proposed.Bidimensional Empirical Mode Decomposition (BEMD) was used to decompose an infrared image into Intrinsic Mode Function (IMF) field.It means that the image was decomposed into a series of Intrinsic Mode Functions and a residue.Then the high frequency IMFs were denoised via wavelet transform,since high frequency components contain much of the noise.The denoised IMFs,together with low frequency IMFs and the residue,were used to reconstruct the image.Experiment results show that compared with the traditional wavelet threshold denoising and BEMD low-pass filtering,the algorithm has better effects on the denoising of infrared image mixed with Gaussian noise and salt & pepper noise.
机译：提出了一种红外图像去噪方法，采用二维经验模态分解（BEMD）将红外图像分解为本征函数（IMF）字段，这意味着将图像分解为一系列本征函数和残差。然后对高频IMF进行小波去噪，因为高频成分中包含大量噪声。去噪后的IMF与低频IMF和残差一起用于图像重构。实验结果表明，与传统小波相比阈值降噪和BEMD低通滤波，对混合高斯噪声和椒盐噪声的红外图像去噪效果更好。

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