Unexpected situations, such as object movement and camera shaking, cause interference and produce blurry, degraded images. These degraded images can adversely affect visual processing tasks, such as target detection and facial recognition. In addition, the super-resolution (SR) of a single image is a basic task to improve the image quality in visual applications. However, due to the existence of artifacts, it is difficult to generate clear SR images from rough and degraded images. In this work, we propose a double-branch projection feedback network that can generate clear high-resolution (HR) images from severely blurred low-resolution (LR) images. One is a deep feature projection feedback branch that can obtain texture-rich SR feature maps and reduce the accumulative errors with depth, and a channel adjustment and multi-scale feature fusion mechanism are proposed to better obtain the mapping relation between LR and HR images. The other is an autoencoder deblurring module, which is used to get deblurred features of an input image. Moreover, a mapping associated feature block is applied to match non-local feature information in the feature fusion module. Extensive experiments show that the network is effective and performs better than the state-of-the-art methods in single image deblurring and SR.