由于成像设备等各种因素影响，图像在成像或传感过程中会受到噪声干扰。图像去噪旨在减少或消除噪声对图像的影响，这一过程往往会导致高频信息的丢失。为了在去除图像噪声的同时保护图像的边缘信息与纹理细节，文章提出了一种计算复杂度相对较低的含有信息保留模块的卷积神经网络，直接对含噪声图像进行降噪。信息保留模块通过残差学习提取局部长路径和局部短路径的混合特征信息。该文采用峰值信噪比(PSNR/dB)和结构相似性(SSIM)两项评价指标对实验结果进行量化，这两项指标值越大，说明去噪效果越好。实验结果表明，在峰值信噪比和结构相似性2项评价指标的均值可达到30.36 dB和0.828 0，相比其他对比算法，2项评价指标分别平均提升了2.15 dB和0.072 9。该算法对不同种类、不同水平的噪声都具有良好的去噪效果，且速度优于所对比的一般算法，对基于卷积神经网络的去噪工作的进一步发展有一定的作用。

Due to various factors such as imaging equipment, the image will be disturbed by noise during imaging or sensing. Image denoising aims to reduce or eliminate the influence of noise on the image, which often leads to the loss of high-frequency information. In order to protect the edge information and texture details of the image while removing image noise, a convolution neural network with information preservation blocks with relatively low computational complexity is proposed to denoise the noisy image directly. The information preservation block extracts the mixed feature information of the local long path and the local short path by residual learning. Peak signal to noise ratio (PSNR/dB) and structural similarity index method (SSIM) are used to quantify the experimental results. The larger the two indexes, the better the denoising effect. Experiments show that the mean values of PSNR and SSIM can reach 30.36 dB and 0.828 0. Compared with other denoising algorithms, the two evaluation indexes are improved by 2.15 dB and 0.072 9 respectively. The proposed algorithm has good denoising effect for different kinds and different levels of noise，and the speed is better than the general algorithms compared, which contributes to the further development of the denoising based on convolutional neural networks.