非线性阈值自调整小波图像去噪方法研究

Image Denoising Method of Nonlinear Threshold-self-adjusting-based Wavelet

为解决小波变换阙值去噪方法中阙值的合理选取，提出一种基于非线性阙值自调整小波变换的图像去噪方法。在传统小波阈值去噪方法的基础上，结合神经网络的非线性双曲线正切函数和BP训练方法，首先对含噪图像进行二进小波分解，然后对分解系数进行小波重建，并将重建系数在BP神经网络中采用最速梯度下降法进行优化处理，得到最优阈值，最后对阈值处理的重建系数进行叠加，得到原始图像信号的估计值，即去噪后的图像信号。仿真实验表明，该方法具有较好的重建图像视觉效果，信噪比（SNR）和峰值信噪比（PSNR）均比传统小波阈值方法提高了1～2dB。

In order to select the appropriate threshold in wavelet de-noising method,a new method for image de-noising based on threshold self-adjustment was proposed in the paper. The algorithm combined wavelet with the nonlinear hyperbolic tangential function in BP neural network based on the conventional wavelet threshold method. Firstly, the wavelet of noisy image was decomposed by binary wavelet transform;Secondly, the partial wavelet coefficients were reconstructed, and the gradient descent method in BP neural network was used to optimize the reconstructed wavelet coefficients to search the optimal threshold;Finally,the new reconstructed wavelet coefficient through threshold was gotten, it was the estimated value of the original image. Experimental results show the better reconstructed image visual effect and demonstrate that the signal-noise-ratio （SNR） and the peak signalnoise-ratio （PSNR） of this algorithm have been improved 1-2 dB in comparison with common wavelet threshold method.