基于小波变换的红外图像去噪

Infrared Image Denoising Based on Wavelet Transform

提出一种基于新型阈值函数的小波域红外图像去噪法,其阈值函数表达式简单且连续,既克服了硬阈值函数不连续的缺点,又克服了软阈值函数中估计小波系数与含噪小波系数间存在恒定偏差的缺陷。同时新的阈值函数还有效地利用了小波系数的成串性,即在小波系数的估计计算中考虑了邻域小波系数的大小。仿真结果表明,在去噪红外图像视觉效果和峰值信噪比两个方面,文中提出的去噪法优于已有的各种门限去噪法和Matlab-wiener 2滤波算法。

Wavelet-domain infrared image denoising based on a new kind of thresholding function is proposed. The proposed thresholding function is simple and continuous. It overcomes the discontinuous shortcoming of the hard thresholding function and the disadvantage of soft thresholding function which is the invariable dispersion between the estimated wavelet coefficients and the wavelet coefficients contaminated by noise. At the same time, the clustering characteristics of wavelet coefficients are utilized effectively in new function. That is, the neighboring wavelet coefficients are incorporated into the estimation of wavelet coefficients. Simulation results show that the proposed denoising algorithm owns better visual effect and PSNR performance than many exiting thresholding methods and Matlab-wiener 2 method.