Recently,deep convolutional neural networks(CNNs)in single image super-resolution(SISR)have received excellent performance.However,most deep-learning-based methods do not make full use of low-level features extracted from the original low-resolution(LR)image,which may reduce the quality of reconstructed image.To address these issues,we propose a method which can connect the low-level features from almost all convolutional layers.Our method use the interpolated low-resolution image as input,employ many skip-connec-tions to combine low-level image features with the final reconstruction process,these feature fusion strategies are based on pixel-level summation operations.After merging the previous convolution features,residual images are used to di-rectly reconstruct high-resolution(HR)images.Experiments demonstrate that the proposed method is superior to the state-of-the-art methods.