【摘要】 提出红外图像去噪方法,将小波变换与广义交叉确认原理相结合,在噪声方差未知的前提下,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正交小波变换后,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,该方法在有效地去除噪声的同时,能较好地保持红外图像的细节信息。算法在性能指标和视觉质量上均优于Donoho提出的小波阈值去噪方法、Johnstone提出经过调整的小波阈值法和传统的中值滤波法。更多还原

【Abstract】 A kind of infrared image threshold denoising method is given. It combines wavelet transform with generalized cross validation. An asymptotically optimal threshold can be determined, without knowing the variance of noise, only using the known input data. After making discrete orthogonal wavelet transform to an infrared image, denoising 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 experimental result ,the given algorithm can reduce the noise of infrared image effectively ,while it also keeps the detail information of infrared image well. As to performance and visual quality, the algorithm is better than the wavelet thresholding given by Donoho, the modified wavelet thresholding given by Johnstone and the traditional median value filtering method.更多还原