传统的图像去噪算法往往仅对平稳或缓慢变化的噪声有效,且残留的图像噪声较大。对此,研究了非平稳环境下基于小波变换的图像去噪算法。该算法根据图像与噪声在小波域的分布特性以及它们的小波变换模极大值随尺度的变化大小不同,运用迭代算法得到不同尺度小波域中噪声的具体位置以及小波系数大小,完成了图像去噪。实验结果表明,对峰值信噪比较低的图像,该方法去噪后峰值信噪比比传统方法的高,并且保留了较多的图像细节,同时对平稳和非平稳的噪声都能进行较好地去噪。

This paper addresses the problem of noise reduction under non-stationary environments based on the wavelet transform.This algorithm can overcome the deficiency of the conventional algorithms of noise suppression,which were only efficient for stationary environments and had large level of residual noise.The algorithm addressed in this paper is based on the different amplitude value change of image and noise and their distributing character in the wavelet domain,by this way,we can find the site and the value of the noise in the wavelet domain by making use of the iterativeness algorithm.Further,noise suppression of the image is implemented.Experiments confirm that the PSNR after denoising with the proposed algorithm is larger than the conventional algorithm;moreover,the high-frequency information of the image contains much information after the noise suppression.At the same time,the noise reduction by proposed algorithm is effective to reduce the noise under both stationary and non-stationary environments.