改进阈值函数的图像去噪方法

Image de-noising method based on improved threshold function

针对传统阈值函数的不连续性、高阶不可导和信号振荡等问题,提出了一种改进阈值函数的图像去噪方法。方法在定义域内连续且高阶可导,表达式构造简单易计算,通过调节参数来控制估计小波系数与噪声污染的小波系数间的偏差,抑制了图像模糊的问题。采用传统阈值函数、半软阈值函数与改进的阈值函数法分别进行去噪的仿真试验,并从主观和客观角度对4种去噪效果进行对比分析。实验结果表明:改进的阈值函数法不仅能有效去除不同程度的高斯噪声,同时能更好地保留图像边缘和细节信息,去噪效果优于传统方法,而且峰值性噪比(PSNR)和均值绝对误差(MAE)的性能也显著优于其他算法。

Aiming at the problems of discontinuity,high-order non-conductive and signal oscillation of traditional threshold functions,an image de-noising method based on improved threshold function is proposed.The method is continuous and high-order derivative in the definition domain.The expression structure is simple and easy to calculate.The deviation between the estimated wavelet coefficients and the wavelet coefficients of noise pollution is controlled by adjusting the parameters,and the image blurring problem is solved.Simulated de-noising by adopting the traditional hard threshold,the soft threshold,and semi-soft threshold method combined with the proposed method.At the same time,the four kinds of de-noising effects are compared and analyzed from the subjective and objective perspectives.Experimental results show that the improved threshold function method not only can effectively remove Gaussian noise of different degrees,but also can better preserve the edge and detail information of the image.This method is high in peak signal to noise ratio(PSNR)and mean absolute error(MAE).Denoising performance is also significantly better than other algorithms.