Image super-resolution reconstruction has a wide range of applications, such as security systems, small object detection, and medical imaging. This study proposes a dual stream feedback network to improve the performance of image super-resolution reconstruction. In the dual-stream network, one path adapts a deep residual dense network to learn the high-frequency information of the reconstructed image, and the other path directly samples the input image to the desired resolution through a sub-pixel convolution layer. Then, the feature maps obtained from the two paths are fused to adaptively selecting the required information. Finally, using a feedback convolutional layer for locally loop training to obtain a large receptive field. By training on the dataset DIV2K, the experimental results show the effectiveness and superiority of the proposed method.