图像在生成或传感过程中往往会受到噪声干扰，噪声干扰会给后续图像处理工作增加难度，甚至会给某些生产活动带来巨大的经济损失。结合平稳小波变换与卷积神经网络的优势，提出了一种有效的图像去噪算法。训练阶段，采用提出的算法对图像进行尺度为1的平稳小波分解后，分别把高、低频分量输入4个设计好的残差网络进行训练；在测试阶段使用小波逆变换来获得最终的预测图像。实验结果表明：在高斯白噪声水平达到σ=50时，去噪后图像的峰值信噪比（peak signal to noise ratio, PSNR）均值和结构相似性（structural similarity index method, SSIM）均值可以达到28.37 dB和0.808 0，提出的算法可以有效去除可见光图像中的高斯白噪声、自然噪声，以及遥感图像在传感过程中产生的噪声，并且在去除图像噪声的同时能较好地保留图像的边缘与纹理细节。

In the process of image generation and sensing, the image is often disturbed by the noise, which will increase the difficulty to the subsequent image processing and even bring the huge economic losses to some production activities. Combined the advantages of stationary wavelet transform with convolutional neural network, an effective image denoising algorithm was proposed. In the training stage, the proposed algorithm was adapted to decompose the image with a stationary wavelet of scale 1, and the high and low frequency components were input into the four designed residual networks for training respectively. In the test phase, the inverse wavelet transform was used to obtain the final predicted image. The experimental results show that when the level of Gaussian white noise reaches σ = 50, the mean of peak signal to noise ratio (PSNR) and structural similarity index method (SSIM) of the denoised image can reach 28.37 dB and 0.808 0. This algorithm can effectively remove the Gauss white noise and natural noise in visible image, and the noise generated by remote sensing image in the sensing process. Moreover, the proposed algorithm can preserve the edge and texture details of the image while removing the image noise.