With the development of modern information technology, people’s demand for high resolution and realistic visual perception of image display devices has increased, which has put forward higher requirements for computer software and hardware and brought many challenges to rendering technology in terms of performance and workload. Using machine learning technologies such as deep neural networks to improve the quality and performance of rendered images has become a popular research method in computer graphics, while upsampling low-resolution images through network inference to obtain clearer high-resolution images is an important way to improve image generation performance and ensure high-resolution details. The geometry buffers (G-buffers) generated by the rendering engine in the rendering process contain much semantic information, which help the network learn scene information and features effectively and then improve the quality of upsampling results. In this study, a super-resolution method for rendered contents in low resolution based on deep neural networks is designed. In addition to the color image of the current frame, the method uses high-resolution G-buffers to assist in the calculation and reconstruct the high-resolution content details. The method also leverages a new strategy to fuse the features of high-resolution buffers and low-resolution images, which implements a multi-scale fusion of different feature information in a specific fusion module. Experiments demonstrate the effectiveness of the proposed fusion strategy and module, and the proposed method shows obvious advantages, especially in maintaining high-resolution details, when compared with other image super-resolution methods.