基于离散平稳小波变换的红外图像去噪

提出了一种基于离散平稳小波变换的红外图像去噪方法。在预先不知道噪声方差的前提下,只利用红外图像的输入数据就可以确定所要求的渐近最优阈值。对红外图像进行离散平稳小波变换后,分别对各个分解层的高频子带利用所提出的方法进行迭代去噪,使各个高频子带分别收敛于其最大信噪比。实验结果表明,所提出的方法在有效的去除红外图像噪声的同时,又能较好的保持红外图像的细节部分信息。算法在性能指标和视觉质量上均优于基于离散正交小波变换的阈值去噪方法和传统的中值滤波法。

Infrared image de-noising based on discrete stationary wavelet transform

A kind of infrared image threshold de noising method based on discrete stationary wavelet transform is given. It combines stationary wavelet trasform with generalized cross validation successfully. An asymptotically optimal threshold can be determined, without knowing the variance of noise, only using the known input data. After making discrete stationary wavelet transform to an infrared image, de noising is done in the high frequency subbands of each decomposition level respectively, so that the maximum signal noise ratio can be obtained in the high frequency subbands respectively. According to the result of experiment, the given algorithm can reduce the noise of infrared image effectively while it also keeps the detail information of infrared image well. In performance and visual quality, the algorithm is better than the wavelet de noising based on discrete orthogonal wavelet transform and the conditional median value filtering method.