Image denoising is the most basic problem and a key technology in digital image processing, which has always been difficult in the field of image processing. The quality of image denoising directly affects the follow-up image processing, such as image edge detection, feature extraction, image segmentation, and pattern recognition. In order to effectively remove the influence of multiplicative noise, we propose a denoising method based on deep residual learning, which solves the problem that the gradient gradually disappears when the number of convolutional neural network's layers increases by residual optimization. By comparing with four classical denoising algorithms, we make the conclusions that the proposed method can not only effectively remove the multiplicative noise, but also preserve the edge of the image and the detail information of the texture area, which will lay the foundation for image segmentation, registration, object recognition, and so on.