基于小波变换阈值萎缩法的红外图像去噪

Infrared Image Denoising Based on Wavelet Transform Threshold Shrinkage Method

针对目前大多数红外图像去噪方法只能集中在空域或频域中进行分析的缺点,提出了一种基于小波变换阈值萎缩法的红外图像去噪算法。该算法具有在时间域和空间域同时进行分析的特性,利用小波变换对确定信号的一种"集中"的特性,使一个信号的能量在小波变换域集中于少数系数上,再对小波系数进行阈值化,可以在小波变换域中除去低幅值的噪声和我们不期望的图像信息。实验结果表明,基于小波变换的阈值萎缩去噪算法能有效的提高图像质量,增强图像的视觉效果,以及准确获取所需要的图像信息。

Most of infrared image denoising methods focus only on the space domain or the frequency domain.Aiming at the shortcoming, an infrared image denoising algorithm is proposed based on wavelet transform thresholdshrinkage method. The algorithm has the characteristic of analyzing time domain and space domain simultaneously,and wavelet transform with a concentration characteristic to the determined signal is used to make the power of a signal on a small number of coefficients in wavelet transform domain. And then, wavelet coefficient is threshold, thelow amplitude noise and unexpected image information in wavelet transform domain can be removed. Experimentalresults show that image quality can be improved effectively by threshold shrinkage denosing algorithm based onwavelet transform. The visual effect of the image is enhanced and the expected image information is obtained accurately.